Anyone searching for a broker or insurer in St. Gallen on ChatGPT or another AI-powered chatbot today will receive a recommendation. But the companies that are mentioned are not chosen at random—it’s a matter of visibility in the new digital age. Fabio Mätzler, founder and mastermind behind cohaga, has developed ranq.ch—a product that addresses precisely this need: a business directory designed not for humans, but for artificial intelligence. Starting July 31, 2026, the platform will go live with profiles of all 744,814 companies in Switzerland.

Fabio Mätzler, founder of cohaga AG, explains why the new business directory ranq.ch wasn’t built for people, but for AI chatbots.

Mr. Mätzler, cohaga is known for LeadHub, an outbound address database for active, outbound customer acquisition. Why is a second product needed now?

Thank you very much for this interesting question and the opportunity to introduce myself in this interview. We developed our new product, ranQ, because we ourselves had a strong need to appear at the top of the results from ChatGPT and similar services—for example, when someone searches for the best company addresses in Switzerland. As early as the beginning of 2023, we were convinced that a large portion of traffic would shift to AI chatbots. Incidentally, according to a McKinsey study, by 2028 over 50 percent of all search traffic will no longer take place on traditional search engines like Google or Bing, but rather on AI chatbots such as ChatGPT, Google Gemini, or Claude. Furthermore, this product expansion was a logical next step, as we use Leadhub to handle outbound leads and ranQ to generate as many high-quality inbound leads as possible for our clients.

You mention a shift from SEO (search engine optimization) to GEO (generative engine optimization). What exactly do you mean by that?

GEO is the next evolution of traditional search engine optimization. The goal is to optimize web content so that it is understood by AI search engines and cited as a source. Leading experts estimate that SEO and GEO overlap by approximately 70 percent. However, there are some important differences, such as the strong focus on regionality and the high effectiveness of comparisons in GEO, as well as the generally higher level of specificity of GEO leads compared to SEO leads.

How exactly does ranQ work?

We create a digital profile for our clients, analyze their areas of activity, and develop targeted content and signals that increase the likelihood that AI systems will recommend this company when relevant questions arise.

Here’s a simplified overview of the process:

A company is registered in the ranQ app; data and positioning are analyzed based on the website; relevant topics and high-volume search queries are identified; AI-optimized content is created and edited by ghostwriters; the content is suggested to the client; content can be customized, deleted, approved, and scheduled; authority signals such as external mentions and backlinks are implemented; and progress is quantified in the audit section and consistently optimized.

The goal: When someone asks an AI chatbot like ChatGPT, “Who is the best painter specializing in historic preservation in St. Gallen?”, the company must not be overlooked.

Where does the data for these 774,000 profiles come from?

The data is based on publicly available company information from our COHAGA Leadhub. We’ve worked diligently to build this database over the past six years. During that time, nearly 20 people have been working simultaneously to ensure our data’s exceptional address quality (operational decision-makers with direct contact information) and timeliness.

Our data is updated through a monthly update cycle via our website and through changes to the commercial register, which are fed into our Leadhub on a daily basis via APIs from the 26 cantonal commercial registers.

Important: A profile does not automatically mean that a company is actively working with RanQ; initially, it is simply a “bare profile.” This means: We’ve laid the groundwork for companies to become visible in this new AI-powered search landscape, but no content is available yet. That only happens once a RanQ plan is signed.

What’s the difference between a “basic” profile and a paid Ranq profile?

A basic profile is a digital business card and includes the physical location, industry, VAT ID, and company size.

A paid profile goes much further: it includes strategic positioning, optimization for AI searches, the creation of relevant content, better semantic classification, building authority, ongoing monitoring, and contact information.

The difference is similar to Google: A listing in the phone book doesn’t mean you’ll be found. Visibility only comes through optimization.

How do you decide which topics to create content for?

We don’t just look at which keywords have high search volume, as is often done in the SEO field—for example, with SEMRUSH. We analyze in detail: What questions are people asking AI systems today? Which companies are currently being recommended? Where are the gaps? Which topics align with the company’s expertise? And: Where is there a realistic opportunity to establish authority?

The focus is on so-called “intent-based” topics—that is, queries that indicate a genuine intent to purchase. In addition, customers can specify the target audiences and product features they want to be found for.

Can you give a specific example?

Let’s take a plumbing company in St. Gallen as an example. In SEO, someone searches for: “Plumbing St. Gallen.” In GEO, someone searches for: “Which plumbing company in St. Gallen West is reliable, as sustainable as possible, and specializes in bathroom renovations?”

An AI uses a variety of signals to make a decision, such as: Is the company relevant to this topic? Is there enough information available? Is it mentioned by other sources? Is there expert content on the subject?

RanQ helps build exactly these signals.

How does this work? How does the AI “learn” which companies are good?

The AI chatbot doesn’t learn like a human, nor does it receive a direct list of recommendations. Instead, large language models (LLMs) analyze patterns: Which companies are frequently mentioned? Which sources are considered trustworthy? What content demonstrates expertise? What connections exist between companies and topics?

You can compare it to a digital reputation. If many independent signals indicate, “This company is relevant to this topic,” then the likelihood that an AI will take it into account increases.

What is the difference between AI-generated responses and Google Ads or traditional advertising?

Google Ads (SEA) buys visibility—the top three search results on Google. When the budget runs out, that visibility disappears. RanQ, on the other hand, builds long-term digital authority. With advertising: “I pay to be shown.” With AI visibility: “The system recognizes me as a relevant answer.”

For AI-generated answers, there is currently no traditional advertising model like Google Ads. Trust, context, and relevance are key—in other words, organic visibility.

You mention backlinks as an important factor. How do you plan to build 100,000 backlinks?

We’ve created a unique concept for this, but unfortunately I can’t reveal too much about it yet. All I can say is this: Part of it will be based on various badges, similar to those used by Kununu or for ISO certifications.

In addition, various business directories, partner networks, regional platforms, industry articles, media mentions, thematic content, and company profiles are, of course, incorporated.

100,000 backlinks are not a one-time effort, but rather a long-term building process spanning many companies and industries.

What subscription plans do you offer?

The model is based on different needs: The Control model is primarily an analysis of the current situation and does not include content. The goal is to assess where you stand and to implement only on-page optimizations on your own website. The price is CHF 950 per year.

The Climb model includes a basic audit and reporting, as well as content in the form of 6 blog posts, 5 FAQ entries, and a comparison page. This model is a good fit if you want a more detailed understanding of where you currently stand and would like to improve your AI score through external content creation and publication on www.ranq.ch. The price for this model is CHF 3,350 per year.

With the Command model, you can expect a professional audit and reporting; the content includes 12 blog posts, 9 FAQ entries, 3 comparison pages, and 3 checklists. The goal here is to gain a very detailed understanding of your current position and to significantly improve your AI score through extensive and high-quality external content creation and publication on www.ranq.ch. The goal is to dominate your own market in terms of AI visibility. This costs CHF 6,710 per year.

Who is ranq.ch primarily intended for?

Primarily for small and medium-sized businesses (SMEs) across a wide range of industries throughout Switzerland.

In particular, companies that currently rely heavily on referrals, Google, or regional recognition. These include: tradespeople, software and IT companies, various service providers, consulting firms and agencies, local businesses, and specialized providers.

Many SMEs offer outstanding services, but digital systems do not yet recognize this expertise.

When ranq.ch goes live on July 31, 2026, what’s the next step for interested companies?

The first step is to check your current AI score here. Are you satisfied with how visible your company is in AI chatbots? After that, your company can decide whether it wants to actively work on its AI presence.

The big change is that companies today no longer optimize just for search engines, but for the answers of the future. Don’t miss out on this historic shift—use the momentum of the current changes to your advantage. Let’s get ranQed!

The questions were asked by Binci Heeb.

Fabio Mätzler, CEO & Co-Founder, B.A. in Business & Economics, University of Basel. When Fabio Mätzler began studying economics at the University of Basel in November 2015, he came across a job posting in which a client wanted someone to create an Excel list of all retirement homes in Switzerland. He applied immediately and was hired. Creating this database marked the birth of his own company. The response to his first databases was so positive that he continuously expanded his services. What began as a student job is now Switzerland’s leading address broker, with addresses from nearly 300 industries, over 2,000 satisfied customers, and just over 30 dedicated employees. Since 2026, Leadhub has not only offered a solution for outbound leads, but with ranQ, the company has also created the opportunity for customers to generate more inbound leads by directing them to the responses of AI chatbots such as ChatGPT and others.

See also: Risk Is Not the Enemy—It’s the Raw Material

The most important moment in an insurance policy isn’t when you sign it. It’s when you need to make a claim. How CBP Finance is rethinking insurance consulting and supporting people exactly when security matters most.

He joined AXA at age 19 and founded two companies of his own in his early thirties. Today, at CBP Finance GmbH, Gürkan Kuyu combines personalized advice with modern technology to create a model that has never existed in this form in Switzerland, and in doing so, poses a simple yet far-reaching question: Why does getting help in an emergency still depend on whether someone answers the phone?

The insurance industry has been debating digitalization for years, usually as a contrast to in-person consulting. Gürkan Kuyu, founder of CBP Finance GmbH and LYA-Icon GmbH, sees things differently. After serving as an agency manager in Rheinfelden and general agent in Aarau—where he was responsible for approximately 80 employees—he developed, together with his longtime colleague Patrick Mahrer, a consulting model that gives customers the choice: in person, by phone, virtually, or digitally. At the heart of the model are a custom-developed app and the high quality of advice provided by the staff. The app combines artificial intelligence with human advice and, in an emergency, provides information even when no one is available at the moment. In this interview, Gürkan Kuyu discusses the transformation of the insurance industry, the future of in-person consulting, the deliberate use of new technologies, and why trust will continue to be built between people in the future.

Mr. Kuyu, what was the deciding factor behind this consulting approach?

Customer expectations have changed fundamentally. Information should be available at all times, and simple questions should be answered as quickly as possible. At the same time, when it comes to important decisions, customers still want to have a personal point of contact.

This becomes most apparent in an emergency. A car breakdown abroad, a hospital admission, water damage. No one wants to start rummaging through insurance documents, looking for a phone number, or getting stuck on hold at a time like that. It’s not about insurance—it’s about peace of mind. And peace of mind shouldn’t depend on whether someone happens to be in the office at that moment.

Our app was developed precisely for situations like these. This is the core of our approach: The customer decides for themselves how they want to receive information—whether digitally, by phone, via video call, or in person.

The focus here is not on technology, but on people. Technology creates flexibility, reduces wait times, and ensures that support is available when it’s needed. Neither the product nor the communication channel determines the nature of the consultation. What matters most are the customer’s needs.

You describe the example of a car breakdown abroad, where the app provides immediate assistance. How does that work from a technical standpoint?

The process is simple, precisely because the situation isn’t. The customer describes what happened in the app. The app knows which policies the customer has on file, checks which ones apply, and tells them what to do next: which benefits are covered, whether and which number to call, whether and where to file a claim, and what they should document. Instead of sifting through files and getting stuck on hold, they’ll have a clear plan of action in two minutes.

We draw the line deliberately and very clearly: The app provides information based on what is stored in it. It does not offer advice.

The difference may seem subtle, but it’s crucial. “Which health plan did I choose?” is a simple inquiry. “Should I change my deductible?” is, on the other hand, a consultation, because it involves your financial situation, your health status, and your plans for the coming years. Such questions are best addressed by an expert and are the result of a consultation.

Before you founded CBP Finance, you held various leadership positions for many years. Which experiences have most shaped your perspective on the industry?

Over the years, I’ve had the opportunity to get to know the industry from a wide variety of perspectives.

I started out in field sales at age 19. That’s where I learned the basics: direct customer contact, needs assessment, the products, the technical expertise, and above all, what good advice means in practice. As an agency manager, I took on responsibility for people—which meant leadership, recruitment, onboarding, building a team, and developing my own network of agents. As a general agent, my perspective expanded significantly once again. You’re no longer just dealing with sales and leadership, but with the entire insurance ecosystem: claims processes, quality management, omnichannel strategies, and the question of how a modern insurance company must be organized to offer customers real long-term value.

I had the opportunity to acquire this knowledge over the course of thirteen years. At the same time, Patrick Mahrer—who had served, among other roles, as deputy general agent, sales manager, and quality manager—and I began to systematically analyze the industry. We weren’t interested in how insurance companies wanted to operate, but rather in what customers actually expected.

Four key needs have emerged:

First: as little hassle as possible. For most people, insurance isn’t a hobby—it’s a necessity. They want simple processes, quick solutions, and minimal red tape.

Second: independent advice. The focus is on the services provided, a fair price, and the assurance that you’re getting the best solution for your specific situation.

Third: a personal point of contact. Artificial intelligence can provide information and speed up processes. But when it comes to important decisions or emotional situations, people want to have an expert they trust by their side.

Fourth: long-term support. Risks and life circumstances change—whether due to starting a family, buying a home, starting a business, or retiring. The expectation is not a one-time contract, but a partner for many years to come.

Our current approach has evolved from these four points. The future of insurance consulting is neither purely digital nor exclusively in-person. It is a smart combination of both.

You led an agency to the number-one spot out of about 300 and were the youngest general agent. What does that say about you, and what does it say about the system behind it?

To be honest, that has more to do with the system than with me.

When I first entered the industry, my circumstances were far from ideal for this profession. That’s exactly why I had to understand sales from the ground up. It became my passion. I analyzed every customer conversation, reflected on successes and mistakes, and consistently documented everything. Over the years, this evolved into a system that I now call the Swiss Sales Clockwork.

I often hear people say things like, “All you need is the right way to handle objections,” or “Sales is all about motivation.” Both of these statements are wrong. Successful sales are not a matter of chance, nor do they depend on a single skill.

But what really made the difference was something else: The system worked not only for me, but for many employees. This enabled us to achieve exceptional results together.

My greatest source of pride, therefore, is neither the top rankings nor the change processes we’ve implemented. My greatest success is the personal growth of our people. Many former employees are now among the top performers in the industry or have taken on leadership roles. I remain in close contact with many of them to this day.

To me, this is the best proof that sustainable success does not depend on individual personalities, but on a system that brings out the best in people. Great companies are not built on individual acts of excellence, but on a culture in which people can grow.

What was your biggest challenge?

By far, the acquisition of the general agency.

At the time, I was faced with a decision: Should I stay at a successful agency that we had built up together over the long term, or should I step out of my comfort zone and take on a much bigger challenge? I made a conscious decision to choose the more difficult path.

To be honest, I found the situation on the ground to be much more challenging than I had expected. It wasn’t just a matter of improving performance. In many areas, there was a lack of structures, processes, and well-established workflows. At the same time, we had a large number of open positions—in leadership, back office, and sales. We had to build everything from the ground up while day-to-day operations continued as usual.

During the first two years, there was hardly a workday that lasted less than 11 to 12 hours. It was an intense period that demanded a great deal of energy, discipline, and passion.

On top of that, our first child was born during this phase. Suddenly, I had to juggle major professional responsibilities with my new role as a father. Balancing the two wasn’t always easy. That makes me all the more grateful to have an understanding wife by my side who has always supported me during this time. Without this family support, many things would not have been possible.

Looking back, I am very grateful for those years. I have learned how important clear structures, consistent leadership, and perseverance are. Above all, I have learned what people are capable of when they take on responsibility, have a clear goal in mind, and are willing to go the extra mile.

By founding CBP Finance, you were able to bring your vision of modern insurance consulting to life. What are you doing differently today by choice?

From the very beginning, we decided not to simply adopt existing processes. Instead, we scrutinized every single step and always asked ourselves the same question: Does this really add value for our customers?

One example is training. In this industry, it’s not uncommon for new advisors to start conducting client meetings on their own after only a short time. We have deliberately chosen not to do this. Before anyone at our company begins working independently with clients, they go through a structured training and onboarding process. The focus is on subject matter expertise, the quality of advice, and understanding people’s needs.

Another difference is the yardstick we use. We don’t measure our success primarily by the number of deals closed, but by our customers’ satisfaction. Customers who receive good advice, are able to build trust, and feel supported over the long term are more likely to recommend us to others. This leads to long-term customer relationships that ultimately benefit everyone.

It’s also important to us that our advice doesn’t end when the contract is signed. Many questions don’t arise until later—when a claim is filed, life circumstances change, or when planning for retirement. That’s when we want to be available.

The future doesn’t lie in selling faster and faster, but in supporting people over the long term. That’s why we consciously invest more time in quality, training, and support.

We don’t believe that trust is built simply by signing a contract. Trust is built through the support we provide afterward. That’s exactly where we want to make a difference.

In today’s insurance landscape, where do customers most often reach their limits?

Not just in an emergency, but also in everyday life. Which franchise did I choose? What coverage applies to this claim? What documents do I need to file a claim? Questions like these are straightforward, but they take time because you have to look up the answers first.

The common thread is always the same: Most people don’t think about insurance until they need it. As long as everything is going smoothly, hardly anyone thinks about their policies.

Safety is one of the most important basic human needs. Especially in situations where uncertainty arises, support must be available easily, quickly, and reliably. Access to important information must therefore not depend on business hours or the availability of a single person.

For us, modern insurance consulting doesn’t begin only when an appointment is scheduled. It begins the moment someone needs support. The key question, therefore, isn’t how we digitize our consulting services, but how we provide reassurance when it’s needed most.

Insurance and financial data are among the most sensitive types of personal information. How do you ensure compliance with data protection and regulatory requirements?

It was clear to us from the very beginning that trust is the most important foundation of our business. Anyone who entrusts their insurance and financial documents to an app must be able to rely on that app to handle this data responsibly.

That is why we did not wait until the end of the development process to address data protection and compliance, but instead incorporated them right from the design phase. Working together with our software developers, external specialists, and legal experts, we structured the entire solution to ensure it meets the requirements of the revised Data Protection Act and the regulatory guidelines for registered insurance intermediaries. The app went live only after all issues had been reviewed and we had received the green light from our legal team.

You mentioned the Swiss distribution network. What exactly does that entail?

The Swiss Sales Framework is the result of over 13 years of practical experience and the analysis of more than 10,000 customer interactions. It is not a theoretical model, but a system that emerged directly from real-world practice—developed for the Swiss and European markets.

Why do I call it the “Swiss Sales Mechanism”? Because, in my view, successful sales work just like a Swiss watch. A watch doesn’t run because of a single gear. Only when all the gears mesh perfectly does the entire system function precisely and reliably.

It’s exactly the same in sales. People often say that success depends solely on handling objections correctly or on a clever closing line. That’s a misconception. Handling objections is just one small cog in the entire system.

Successful sales begin much earlier—with the right mindset, preparation, and first impression. They range from needs analysis, through communication, building trust, and the quality of advice, all the way to follow-up support, long-term customer loyalty, and much more. Only when all these gears mesh together can sustainable sales success be achieved.

We have spent many years developing, analyzing, and continuously improving this very system. Every insight gained from thousands of conversations and from leading sales teams has been incorporated into this mechanism.

The Swiss sales mechanism goes far beyond sales themselves. In addition to the sales function, there are other mechanisms for leadership, recruitment, onboarding, management, marketing, and performance improvement. After all, a company can only achieve sustainable success if not only sales are functioning well, but the entire system behind them as well.

And this is precisely where a crucial key lies: The Swiss Sales Engine is not just a system, but also a clear developmental framework. It shows, step by step, how individual employees can become true top performers. It explains how the journey from a newcomer to a consistently successful salesperson works—and also how good salespeople can become strong leaders who, in turn, can build and expand teams themselves.

This development is no coincidence, but rather the result of a clear path that is firmly anchored in the Uhrwerk system. Each phase builds on the previous one, each skill is developed in a targeted manner, and each level brings the individual closer to their full potential. This is exactly how, over the years, we have seen employees not only improve their sales performance but also develop into top salespeople and, later, successful leaders.

For me, the Swiss Sales Engine is therefore not a course or a single method. It’s a shortcut. It brings together the experience we’ve gained over many years into a clear, tried-and-true system. Instead of every salesperson or entrepreneur having to make the same mistakes on their own, they receive the tools, the structure, and the crucial keys that have already proven themselves in practice—presented in a way that’s easy to understand, well-organized, and immediately actionable.

In addition to CBP Finance, you are the owner of LYA-Icon GmbH. Why was this second step important to you?

Besides the insurance industry, helping people grow in sales is my second great passion.

With LYA, we’re therefore passing on the sales know-how to sales organizations and companies that want to further develop their own sales structures. Looking back, I often think about how valuable it would have been if someone had explained certain concepts to me back then and saved me from taking a few detours. That’s exactly the kind of added value we want to offer.

The LYA is therefore aimed at both sales professionals and companies that wish to develop their organizations in a sustainable manner.

Back then, I felt like I had to find the key to every door myself. Today, we want to help people and companies open those doors more quickly—not by doing the work for them, but by sharing experiences that save them time and help them reach their goals faster.

What will the insurance advisor of the future look like?

I am convinced that personal insurance advisors will continue to play a central role in the future. Insurance and finances are among the most important decisions in life. When it comes to protecting their families, planning for retirement, or managing their own businesses, many people will continue to want a trusted advisor who can guide them and understand their personal circumstances.

At the same time, I believe the profession will undergo significant changes. Digital solutions and artificial intelligence will take over many administrative tasks and be able to answer simple questions. This will give advisors more time for what really matters: understanding their clients’ needs, explaining the big picture, and developing long-term solutions.

I expect the industry to consolidate in the coming years. In my view, the market could function with about 30 percent fewer consultants in the future than it does today. Not because in-person consulting is becoming less important, but because technology is taking over many routine tasks and enabling more efficient processes.

That is precisely why the quality of the advice will be crucial. Advisors who focus primarily on closing deals quickly and lose touch with clients after the contract is signed will find it increasingly difficult. In my view, the future belongs to those who support their clients over the long term, understand their needs, and create genuine added value.

Sustainability, the quality of our consulting services, and customer loyalty will be the key factors for success. Those who build trust and consistently put the customer first will continue to be successful in the future. As a company, we are already preparing for this trend today.

Binci Heeb asked the questions.

Gürkan Kuyu knows the Swiss insurance industry from nearly every angle. At age 19, he joined AXA as a field representative, later led the Rheinfelden agency to first place in a national competition among approximately 300 agencies as agency manager, and, as the youngest general agent, took on responsibility for about 80 employees in Aarau, where he successfully restructured the general agency. His areas of expertise include sales, building and leading sales teams, improving performance, and change management. Over the course of thirteen years, he developed this into the “Swiss Sales Engine,” a development system for sales, leadership, and corporate culture, which he now shares through LYA-Icon GmbH with sales professionals and sales organizations, whom he coaches and supports in the process. Through CBP Finance GmbH, he also runs an independent insurance brokerage registered with FINMA, complete with its own app, and is building a model in which customers receive round-the-clock support, either in person or via state-of-the-art technology. His guiding principle gives CBP Finance its name: Customer Before Product.

See also: A New Approach to Claims: How Vectoryx Orchestrates Claims Settlement

Two to four yes/no questions, a real-time analysis, no AI model in the traditional sense, and yet an accuracy rate that’s causing a stir among insurers. Manjit Rana, Executive Vice President of Insurance at Clearspeed, explains in an interview withthebrokernews how neuroscience-based technology is revolutionizing the fight against fraud, and why the Swiss market has long been more than just an insider tip for Clearspeed.

On 9 June 2026, Manjit Rana was a guest at the “Reinsurance Rendez-vous Zurich,” organised by the Swiss InsurTech Hub in collaboration with the Boston Consulting Group. What he presented there sounded, at first glance, like science fiction: a technology originally developed for military special forces and now used in day-to-day insurance operations to detect fraud based on the human voice without biasbiases. We asked him about it.

Manjit, Clearspeed has its roots in the military sector. What does a technology developed for special forces have to do with the day-to-day work of insurance brokers and claims handling teams?

The underlying challenge is the same: screening large numbers of people quickly and fairly, without relying on background or profile. In a military setting, that means vetting recruits who may gain access to sensitive intelligence or weapons. In insurance, it means assessing claimants or applicants at speed and without bias. In both cases, Clearspeed helps determine who or what to trust through voice, rather than by inferring risk from demographic or historical data.

Clearspeed unlike other predictive models that leverage AI, Clearspeed is grounded in neuroscience. To outsiders, that sounds like a significant difference, but why exactly? What makes this approach fundamentally different?

Insurers already use AI to sharpen fraud models, using larger datasets to build tighter segments based on age, location, historical behaviour or claims timing. Clearspeed takes a different approach: The signal comes entirely from the voice.

The signal itself: when someone answers a question where something is genuinely at stake, the brain reacts involuntarily, producing measurable changes in the voice. Clearspeed reads those changes, not the words spoken, but a signal. This technology is grounded in decades of neuroscience research.

How it’s captured: a short set of automated yes/no questions, delivered by phone or online, with no human interviewer. That removes interviewer bias and ensures every person is assessed identically.

What sets it apart: most tools look backward, using background checks, credit scores, or pattern-matching against records. Clearspeed provides an in-the-moment signal that cannot be coached, profiled, or falsified.

Key properties: the signal sits below the level of language, so it’s language agnostic and is also agnostic to gender, race, and location. Risk is flagged at the level of each individual question, not just the overall session, giving teams precision rather than a blunt score.

The output: a result returns within seconds, minimal risk or elevated risk. Clearspeed does not make the decision; it tells your team exactly where to focus.

You describe your technology yourself as a ‘metal detector’: not a judgement, but an early filter. How do you ensure that this metaphor delivers what it promises in practice?

A metal detector doesn’t decide whether a passenger flies; it signals whether metal is present, so security staff know who needs a closer look. Clearspeed works the same way. It signals whether a risk indicator is present in response to questions tailored to the specific risk being assessed, whether that’s a claim, an application, or an onboarding decision. It never determines the outcome. It tells the organisation, with a high degree of accuracy, whether a case can be fast-tracked or needs to follow the usual human review. Because the questions are built around the specific risk in question, the same approach adapts to virtually any use case across the insurance value chain.

A 20- to 30-fold return on investment is a strong claim. Can you illustrate this with a specific example from everyday insurance practice, and what changes for a claims handler working with Clearspeed?

The return isn’t purely about catching more fraud, that’s the common misconception. It comes from three sources at once. First, fraud detection itself: high-risk claims are flagged early and investigated properly, stopping claims that would otherwise be paid simply to avoid the cost of a dispute. Second, operational efficiency: when the majority of claims clear as low-risk within seconds, skilled investigators spend their time only on the minority that genuinely need them, lowering the cost per fraud detection substantially. Third, and often overlooked, is speed of settlement for genuine customers. Allianz UK saw a 59 per cent lift in immediate claims settlement alongside a 36 per cent lift in average fraud-loss savings per claim. As Allianz stated: fraud solutions don’t need to disrupt the customer journey, they can improve it. The 20 to 30-fold average return across our client base reflects the compounding effect of all three factors, set against a low unit cost for the technology itself.

Clearspeed is used for claims management, underwriting and onboarding. Where does the greatest leverage lie today, and where do you see untapped potential?

Today, the greatest leverage lies in claims (FNOL) and fraud detection, where most of our insurance clients began, with growing extension into underwriting and point-of-sale. The untapped potential extends further across the value chain: identifying employees, including remote call-centre staff, who may be capturing and selling claims data to disreputable third parties; and detecting cases where an applicant or intermediary withholds risk-relevant information to secure a cheaper premium. Wherever an organisation needs a fast, unbiased answer to whether a risk signal is present, there is a role for Clearspeed.

Partnerships such as those with Allianz or Zego show that Clearspeed is already well established in the UK market. What is required technically, organisationally and culturally for an insurer or reinsurer to really get off the ground?

Clearspeed is well established across the UK and US, partnering with the majority of major P&C insurers in the UK, with growing deployment across Europe, Asia, and Latin America. Technically, the barrier to adoption is low: the solution runs through a web browser, so an insurer can be live and deriving value without IT resources or integration into policy or claims systems. Deeper integration adds further value as usage scales, but it isn’t a prerequisite to start.

Culturally, very little needs to change. Applicants or claimants who clear the automated questionnaire can be fast-tracked to settlement or onboarding, improving the experience for genuine customers. Investigation teams gain the confidence to focus their time on the cases that flag elevated risk, and can spend more time and empathy with the customers who need it. Both effects reinforce each other from day one.

At the ‘Reinsurance Rendezvous Zurich’, you were a guest of the Swiss Insurtech Hub. How do you perceive the Swiss market, and what distinguishes it from the UK market or other EMEA markets in which you operate?

The Swiss and UK markets are both mature and well regulated, but structurally distinct. Nineteen of Switzerland’s 26 cantons run monopoly cantonal insurers for building and natural-hazard cover, and compulsory, community-rated health insurance (LAMal) has no UK equivalent. Switzerland also punches above its weight in reinsurance, with Zurich the world’s third-largest hub. Distribution is relationship-driven, with around 1,000 FINMA-registered brokers serving a stickier, lower-churn market than the UK’s price-comparison-led, increasingly digital-direct model.

The contrast sharpens around fraud. The UK reports hard detected figures, £1.16 billion in 2024 from roughly 98,400 fraudulent claims and 684,800 blocked applications, while Switzerland estimates around one in ten claims payouts involves fraud, a higher claimed prevalence despite the UK catching more in absolute terms. The difference is infrastructural: the UK has a mandatory, centralised claims record, an industry fraud bureau, and a dedicated police unit; Swiss data-protection law sets a high bar on cross-insurer data-sharing, and the industry’s shared tool covers only the eight largest carriers. This is exactly where Clearspeed adds the most value: because our signal requires no external or personal data, it lets insurers focus investigation resources precisely where the risk is real, regardless of how fragmented the surrounding data infrastructure may be.

Clearspeed completed a Series D funding round of 60 million dollars in 2025. What will this capital be used for, and what role does the DACH region play in your growth strategy?

The raise funds international growth across our two core markets: Government, Defence & Security, and Enterprise, the latter weighted heavily toward insurance and banking. DACH, and Switzerland specifically, is a clear priority within that expansion, given the market’s sophistication and its distinct fraud and regulatory profile.

Fraud is becoming increasingly sophisticated; deepfakes and AI-generated voices are no longer the stuff of science fiction. How does Clearspeed stay one step ahead of a threat landscape that is itself evolving with the help of AI?

Long procurement cycles mean insurers are almost always working with fraud-detection tools that lag the sophistication available to a determined bad actor. Clearspeed sidesteps the AI arms race by design: an individual knows if they have manipulated evidence, and Clearspeed sits before the submitted evidence is even examined. If no risk signal is detected, a claim can be fast-tracked without the cost of sourcing and reviewing evidence. If a signal is detected, the investigation team can apply the full range of tools, including expert review, to the claim and its evidence. This is particularly effective for exaggerated property claims, where the claimant genuinely owns the item and holds a valid receipt but knows the item hasn’t actually been lost or stolen.

Finally: if you could give a Swiss insurance broker, who is hearing about Clearspeed for the first time, a single piece of advice, what would it be?

Innovative solutions are rarely built specifically for insurance. More often, they’re adapted from thinking and technology proven against similar challenges elsewhere. Clearspeed was built to assess risk in high security and military screening, and has since been deployed across government, probation services, sports integrity, insider threat, banking, and insurance, worldwide. The organisations that benefit most are the ones willing to look outside their own industry for a solution already proven elsewhere.

My advice: stay open to trying it. Every significant shift in an industry looks unnecessary right up until it becomes standard practice. The value is best understood in a conversation, not a brochure, and that’s a conversation we’d welcome.

Binci Heeb asked the questions.

Manjit Rana has been shaping the UK insurance industry for nearly four decades. From early broker software and reporting platforms to telematics, the rise of insurtech, and the fight against AI-driven fraud schemes, he has consistently been ahead of the curve. As EVP of Insurance at Clearspeed, he works with insurers to address emerging challenges in fraud, claims, and customer trust through innovative technology solutions. He is a globally recognized thought leader in insurance innovation, an entrepreneur, and a conference speaker with deep expertise across insurance markets in the UK, the US, and APAC. Manjit has mentored founders, advised boards, and serves as an Entrepreneur-in-Residence at the University of Nottingham. Most recently, he was and was named Insurance Personality of the Year at the 2026 British Insurance Awards.

See also: AI in Reinsurance: Between Hype and Measurable Added Value

Ten years between good health and the end of life a period that hardly any insurance company factors into its pricing. Nadine Esposito, founder of Wellthspan Advisory, discusses an industry that still thinks in terms of “lifespan,” while its customers have long since been living in “healthspan.”

There are terms in the insurance industry that become outdated faster than the people they’re meant to describe. “Longevity risk” is one such term: technically correct, but blind to what happens in between. Nadine Esposito has coined a distinction for this that is uncomfortably simple. Lifespan is the length of time a person lives. Healthspan is the portion of that time during which they are healthy. And in between lies the sickspan, on average, a decade, during which chronic illnesses, the need for long-term care, and associated costs converge, a reality that hardly any product on the market today addresses.

Esposito, founder of Wellthspan Advisory, has been exploring for years what demographic change means for an industry that traditionally views risk through the lens of mortality tables. Her thesis: Longevity is not just a pension issue, but a systemic risk that is also one of the greatest untapped business opportunities of the coming decades. In this interview, she explains why technical knowledge can be replaced but judgment cannot, why the fastest-growing customer group is largely ignored by product development, and what it means when an industry outlives its own plans.

Ms. Esposito, you distinguish between lifespan, healthspan, and sickspan. Why is the traditional definition of “longevity risk” no longer sufficient in the insurance industry?

Because it takes only a single number into account: how long someone lives. The classic longevity risk is the risk that people will live longer than the mortality table assumes, in other words, a purely pension-based perspective. It is technically correct, yet it fails to account for what is most costly in economic terms: the quality of those additional years.

The data is clear. Across 183 WHO member states, the gap between life expectancy and healthy life expectancy averages 9.6 years; in the U.S., it is 12.4 years. I call this decade the “Sickspan”: the phase in which chronic illnesses, the need for long-term care, cognitive impairments, and the highest costs all coincide. From an actuarial perspective, the Sickspan is the actual event, and yet hardly any product covers this transitional period. We insure against death, and we finance retirement. In between lies a decade that neither life insurance nor traditional health insurance adequately addresses.

Anyone who measures longevity solely in terms of lifespan misses the risk precisely where it arises. That is why the industry needs this three-part framework: lifespan as the time horizon, healthspan as the usable time, and sickspan as the phase for which products, reserves, and advice are largely lacking today.

The average duration of sick leave is ten years, and in the U.S., it is actually continuing to rise. What are the factors driving this trend, and can it be applied to Europe?

Paradoxically, the most important driver is medical success. Today, we survive conditions that killed previous generations, such as heart attacks, many types of cancer, or strokes. Fatal conditions are becoming chronic conditions. Medicine is prolonging life faster than it is prolonging health. Added to this are metabolic diseases and physical inactivity, which cause morbidity to set in earlier, as well as a social gradient: preventive care reaches precisely those who need it most but are least likely to receive it. The fact that the U.S. tops the list at 12.4 years has a lot to do with unequal access to preventive care and primary care.

The trend is applicable to Europe, but not the extent of it. European systems mitigate the gradient, but they do not eliminate it. And Switzerland should not rest on its laurels: 49 percent of the population has low general health literacy, and the proportion is even higher when it comes to digital health tasks. Prevention is a personal choice, but it is shaped by structural factors. Those who cannot navigate the healthcare system lose healthspan, regardless of their financial means. For insurers, this means that “sickspan” is not an American phenomenon to be observed from a safe distance. It is growing here as well, just more slowly and quietly.

If longevity is a systemic risk, who in an insurance company should really be addressing it today: the actuarial department, product development, HR, or senior management?

The honest answer: all four, and that’s exactly the problem. What belongs a little bit to everyone ultimately belongs to no one.

“Systemic” means that the risk manifests itself in different forms across every function. In actuarial work, this raises the question of whether models take morbidity as seriously as mortality, in other words, whether we can price sick leave, not just death. In product development, it’s the gap between what the 50-plus generation needs and what’s actually on the shelf. In HR, this issue is twofold: the company’s own workforce is aging, experienced specialists are retiring, and their knowledge often goes with them. And in executive management, it’s a strategic question of whether to manage demographic change as a cost issue or shape it as a growth opportunity.

My recommendation is therefore clear: Responsibility belongs with senior management, with an explicit mandate that cuts across silos. Not because the functional departments aren’t capable of handling it, but because longevity happens precisely at the intersections: between health and finance, between human resources and product. Silos can solve silo problems. Sick leave is not one of them.

You founded your own consulting firm, Wellthspan Advisory, to address this topic. What was missing that led you to want to fill this gap, and how does your approach differ from traditional actuarial or strategy consulting?

What was missing was consulting that considers financial and health issues together. Actuarial consulting does an excellent job of optimizing within existing models, but the models themselves address the issue of pensions, not the issue of health insurance. Strategy consulting presents the “demographics” megatrend in a slide deck, but rarely translates it into products, advisory sessions, and personnel decisions. That was the gap.

Wellthspan Advisory works with a framework I call Longevity Literacy 2.0: four forms of capital: financial, health, social, and structural, and two mechanisms that connect them. First, these forms of capital shift with age and in response to shocks: diagnosis, the need for long-term care, and death. Second, they convert into one another: financial resources are replaced by health, and care provided by family members replaces money but comes at the cost of earned income. Traditional financial planning treats each of these dimensions in isolation. However, the decisions with far-reaching consequences over the course of a long life occur precisely at these transition points.

The second difference is the target audience within the company. I work not only with the actuarial department, but also with the people who interact directly with customers, such as advisors and relationship managers. That’s where it’s decided whether longevity remains just a concept or becomes a topic of conversation.

They say that technical expertise can be replaced by AI, but judgment cannot. Where exactly does that line lie in practice?

The line is drawn where knowledge ceases to be codifiable. Mortality tables, product rules, regulations, tax logic: all of these are documented knowledge, and today, AI can retrieve documented knowledge faster and more consistently than any human. Anyone who bases their professional value solely on this has a problem.

Good judgment is revealed in what I call “moments of shock”: a diagnosis, a family member requiring care, or a death. In these moments, decisions regarding various forms of capital, such as money, health, relationships, and legal structures, are compressed into short periods of time under intense stress. That’s when you need someone who listens to what the client isn’t asking.

A real-world example: An AI accurately calculates a retirement gap. What it fails to recognize is that behind a customer’s seemingly technical product inquiry lies his wife’s emerging cognitive decline, and that the truly urgent issues are power of attorney, full access to bank accounts, and long-term care financing. Recognizing this requires not computational power, but experience with people in exceptional situations. In short: knowledge scales, but judgment does not. For companies, this leads to an uncomfortable reality: the value of experienced employees increases as AI takes over the technical tasks. Yet many are currently planning in the opposite direction.

What exactly happens in a company when experienced professionals retire without a succession plan in place? Can you describe an example from your consulting experience?

The tricky part is that none of this appears on a balance sheet. There is no line item labeled “lost experiential knowledge.” The costs show up in scattered ways, in the form of longer lead times, lost bids, or legal risks, and are rarely traced back to their common cause.

The irony is hard to miss: An industry whose business is assessing risks leaves one of its biggest risks uninsured within its own ranks. Knowledge loss is more predictable than almost any other risk, since the retirement date is set years in advance. It’s not a lack of predictability, but a lack of priority.

Companies are looking for a 35-year-old with the knowledge of a 55-year-old. How have you encountered this line of reasoning in your clients’ cases?

The fallacy behind this is more serious than the smile might suggest: experience is treated as a personal trait rather than as a function of time. Experience cannot be compressed. You can’t “buy” it when you’re younger; you can only make it transferable.

That’s exactly where I start in my consulting engagements. Instead of looking for the “impossible” person, we seek out the “possible” structure: tandems in which an experienced professional and a younger colleague share responsibility. Roles that are divided into those requiring experience and those requiring technical skills. And experiential knowledge that is recorded as documented decision-making cases, not as a process manual that no one reads, but as a collection of real-world edge cases with justifications. There’s no such thing as a 35-year-old with the knowledge of a 55-year-old. Any organization can build a team that combines both, and it can do so today.

They are calling for a shift in thinking about succession planning, away from linear career paths. What does a succession model look like that anticipates career breaks and fresh starts, rather than treating them as exceptions?

The classic succession model is a ladder: training, promotion, handover, retirement: a linear career followed by a cutoff date. This model assumes a career path that is becoming increasingly rare. With a working life spanning nearly fifty years, disruptions, such as a break to care for a family member, an illness, or a career change at fifty, are not the exception; they are the norm. Incidentally, this is the same logic as in my framework: shocks are not disruptions to the plan; they are part of one’s career path. Anyone who plans for the average is planning wrong.

A succession model that accounts for this has four components. First, pipelines instead of “crown princes”: multiple candidates of different ages for key roles, so that a single absence doesn’t derail the model. Second, genuine return pathways: Anyone who steps away for two years to provide care returns to the main track, not a siding. Otherwise, we systematically lose the women of the sandwich generation. Third, gradual transitions instead of a cut-off date: partial retirement with an explicit mandate for knowledge transfer, in which the final years are devoted to passing on expertise, not to winding down. Fourth, “encore” roles: retirees who return for leadership positions, projects, or training.

None of this is exotic. What’s exotic is just how rarely it’s built systematically.

Today, four or five generations work at the same company. Where does this actually lead to friction, and where is it more a matter of prejudice?

Let’s draw a clear line. Real friction arises in communication standards, whether synchronous or asynchronous, phone calls or chat, meetings or documents. In the choice of tools. In the pace of decision-making. And in differing expectations regarding loyalty and tenure: Someone who plans to stay with a company for forty years will find it hard to understand someone who thinks in three-year increments, and vice versa. These are real coordination costs, and they’re resolved through explicit agreements rather than appeals.

In contrast, almost everything related to performance is considered a prejudice: that older people don’t want to learn, can’t handle technology, or resist change. The evidence for this is thin; the differences within an age group are consistently greater than those between groups. Age is a weak predictor of almost everything that matters in everyday work life.

The real issue runs deeper: Most so-called “generational friction” isn’t friction at all. It’s friction between structures designed for a three-stage “normal” life course (training, working, retirement) and workforces that have long since been living differently. When career logic, continuing education budgets, and performance evaluation systems recognize only linear advancement, the system creates conflicts that are then conveniently attributed to generational differences. Prejudice is easier than structural reform, but only in the short term.

In your experience, what organizational structures are most often missing that would allow the experience and knowledge of older employees to truly combine with the technological expertise of younger employees, rather than just existing side by side?

More often than not, shared responsibility is lacking. Most companies have mentoring programs, and most of these programs are mere rituals: people meet, they talk, they go their separate ways, but the knowledge stays right where it was. Knowledge doesn’t flow through conversations about work, but through working on the same problems with shared risk. The most effective structure is therefore a mixed team with shared accountability for results: it’s not the older person advising and the younger person doing the work, but both taking responsibility for the same outcome.

According to this, three unspectacular things are missing. First, time: Knowledge transfer, which is supposed to take place alongside the full workload of daily business, simply doesn’t happen. Transfer requires a time budget just like any other project. Second, incentives: As long as performance evaluations focus only on what an individual produces on their own, sharing knowledge is individually irrational. Those who make their knowledge transferable must be rewarded for it, not just commended. Third, a record: There needs to be a documented archive of decisions that is, real borderline cases with explanations of why those decisions were made. Processes can be written down; sound judgment can only be demonstrated through specific cases.

None of this is expensive. What is expensive is the alternative: two workforces working politely side by side until one of them retires.

How should continuing education or knowledge transfer be structured so that it works in both directions, rather than just thinking in terms of “from young to old”?

First, regarding perspective: Remarkably, most programs think in only one direction, whichever is most convenient, depending on the topic. When it comes to digital skills, the “young” are supposed to train the “old,” and when it comes to technical skills, the “old” are supposed to train the “young.” In both cases, one side is treated as the group with deficiencies. That is the fundamental flaw.

Effective knowledge transfer is an exchange between equals: technological expertise in exchange for judgment, with both sides serving as both teachers and learners. This works best not in the classroom, but through real-world cases: a tandem team working together to resolve a complex claim conveys more knowledge in both directions in three weeks than two separate training programs do in a year. Practical knowledge cannot be passed on through a manual; it can only be acquired through collaborative case work. And digital competence doesn’t stick when it’s presented as tutoring, but rather when it solves a concrete problem encountered in real life.

Behind this lies an economic calculation: Many companies hardly invest in continuing education for employees over fifty because it “no longer pays off.” This calculation stems from a time when retirement began at 62. Someone who works until age 70 still has fifteen working years ahead of them at age 55, more than some young employees will even stay with the company. The logic behind the payback calculation isn’t wrong. It just’s based on the wrong career trajectory.

You mention one of the biggest untapped business opportunities of the coming decades. What specific product gaps do you see today among insurers for the 50-plus or 60-plus generation?

The biggest gap is the “Sickspan” itself: the transition period between “healthy” and “in need of care,” which lasts an average of a decade. There are hardly any products available today to address this: health insurance covers treatment, long-term care insurance kicks in late, and in the meantime, families bear the costs themselves. Specifically, there is a lack of: bridging products for the early care phase, hybrid products that reward prevention rather than just paying for damages, and solutions for chronic disease management.

Second gap: cognitive impairment. This is a major financial issue: decision-making capacity, powers of attorney, access to bank accounts, investment decisions, and yet, from a product perspective, this area is virtually untapped. “Supported banking” and insurable retirement planning structures for cases of cognitive impairment could constitute a separate business segment.

Third gap: the sandwich generation. Anyone caring for their parents at age 52 is converting earned income into unpaid care work. There is virtually no system in place to provide financial security during care breaks that bridges gaps in income and retirement savings.

And fourth, the wealth side: The largest transfer of wealth in history is, to a significant extent, an intra-generational transfer: in the U.S., of the estimated 124 trillion dollars by 2048, about 54 trillion will first go to spouses, mostly women. Products and advice tailored specifically for this moment, when someone is widowed, wealthy, and often making major decisions on their own for the first time, are surprisingly rare.

This customer group holds the lion’s share of private wealth, but according to you, it is largely ignored by product development. Why is that? Is it due to the data available, a focus on target groups, or a lack of internal demand?

All three, but not in equal measure. The strongest factor is target-group thinking. The industry’s acquisition strategy is optimized for younger segments: that’s where long-term contracts are signed, and where customer value pays off over decades. A 60-year-old woman is considered “fully insured”, an existing client to be managed, not a market to be developed. The fact that this very customer is facing the most expensive and complex decisions of her life, long-term care, estate planning, housing arrangements, and cognitive health planning, is not factored into this logic.

The data situation plays a role: For the transition zone of the Sickspan, morbidity data are scarcer than mortality data, and insurers are reluctant to price what is difficult to model. This is a reality, but it can be resolved. The data gap is more of an excuse than a cause.

What’s most interesting is the lack of internal demand. Product success is measured by new business from younger cohorts; anyone who proposes a product for people 60 and older internally is working against their own success metrics. And there is simply a lack of longevity expertise on the committees: Only about one in four adults is “longevity literate,” and there is no reason to assume that product committees are an exception. You don’t develop products for a risk that you yourself don’t understand.

What distinguishes this generation’s expectations from those of their parents or grandparents at the same age, particularly with regard to health, travel, and lifestyle?

The key difference: This generation expects to shape their later years, not just manage them. For their parents, retirement was a time of rest: they withdrew from active life, and their expectations for the years ahead were modest. Today’s 60-year-olds plan for twenty to thirty active years: travel, not as a one-time reward but as an integral part of life; working beyond retirement age, often in new ways; second careers, continuing education, and entrepreneurial projects.

The shift is most evident in the area of health. The grandparents’ generation was made up of patients. Health was something the doctor managed. This generation is actively involved: prevention, wearables, health data, targeted information. At the same time, the demand for self-determination is growing in areas where things get serious: housing in old age, care, and end-of-life decisions. People want to decide for themselves, not have decisions made for them.

For insurers, this has two uncomfortable consequences. First, this customer doesn’t compare her experiences with insurance from 1995, but rather with the best digital services she uses every day. Second, this group is more heterogeneous than any target group before it: 60 is no longer a life stage, but simply an age. There’s a world of difference between the marathon runner and someone in the early stages of needing long-term care: from an analytical perspective, maintaining a “seniors” segment is about as precise as a “adults” segment.

When you look back in five years: How would you determine whether an insurance company has embraced demographic change as a strategic transformation or continues to treat it as an HR issue?

Four things, all of which can be observed. First, its integration: Is longevity embedded in senior management with its own responsibilities and budget, or does it appear once a year in HR reporting under “age structure”? Second, look at the product lineup: Are there offerings that address healthspan – not just lifespan – as well as bridging sick leave, prevention, and cognitive wellness, as measured by new business with people over 50? Third, in the workforce strategy: Does it account for transitions, such as return-to-work programs, phased retirements with knowledge transfer mandates, and documented experiential knowledge, or does it continue to quietly let its most valuable capital slip away each year? And fourth, regarding the advisory process itself: Do client meetings address all four forms of capital—money, health, relationships, and structures, or do they continue to focus solely on the portfolio?

If you want a quicker test, you need only ask one question: How many sick leave years do you project for your own customer base, and which of your products cover them? An insurance company that has an answer to that question has understood the shift as a transformation. One that first has to figure out who’s responsible for it will also answer the question, just not in the way it intends.

Binci Heeb asked the questions.

Nadine Esposito is the founder of Wellthspan Advisory, a consulting firm based in Switzerland and Australia that advises insurers, financial institutions, and other companies on longevity and demographic change. She developed the framework, which integrates financial, health-related, social, and structural resources across the entire life course, and is the author of a scientific publication on the subject. By distinguishing between lifespan, healthspan, and sickspan, she has coined a terminology that places the gap between life expectancy and healthy life expectancy at the center of product and strategy discussions.

See also: Live Longer, Plan for a Shorter Life?

Vectoryx positions itself as a regulated multi-party infrastructure for claims processing and, among other things, uses ChatGPT integration to bring the initial claim report directly into the dialogue with those affected. Co-founder Alexander Stade discusses media discontinuities, interoperability, and why claims management rarely fails due to regulatory issues.

Property damage claims are complex events involving many parties: owners, residents, property management companies, brokers, insurers, and restoration partners. This is precisely where Vectoryx comes in with “Claims,” a claims settlement product that connects all parties in real time. With its own app in the ChatGPT Store, the company is also taking an unconventional approach: turning the claims filing process into a dialogue-based AI experience. In an interview withthebrokernews , co-founder Alexander Stadeexplains where the actual pain points in the claims process lie, how regulated infrastructure and AI interfaces interact, and where Vectoryx aims to go as a company.

What was the specific moment or experience that led to the founding of Vectoryx?

Vectoryx was born out of my own personal experience. Over several years, I built up my own property management business and later sold it. That’s exactly where the pain point arose: claims management is an extremely complex process involving a great many parties. And even after consolidating all the information, created a clean database, and submitted it to the insurer, it still took another two to ten months to receive approval to begin the work which severely undermined the satisfaction of everyone involved and ultimately actively “cost” all parties customers.

So, on the one hand, we faced enormous communication challenges when claims arose; on the other hand, we faced enormous coordination challenges such as bringing appraisers or restoration contractors together with those affected, and then figuring out how to get back on track with the process afterward. One of the co-founders comes from the insurance brokerage sector and faced exactly the same coordination and communication challenges with claims involving single-family homes as we did in the housing industry. Added to this is the economic reality: Handling claims is labor- and IT-intensive, but is generally not compensated separately; instead, customers view it as an included service. High effort, no return. That’s how the company came to be. We mapped out the entire process, spoke with damage restoration contractors, insurers, and other property management companies, and used that input to develop our first product, which has evolved significantly since then.

You say that claims management rarely fails because of the claims settlement process itself, but rather because of a lack of information or unsystematic information. Can you illustrate this with a typical real-world example?

Let’s take water damage as an example. At a very high level, this is how it works: The damage is reported to the property manager, who documents it and then coordinates the response. He brings the damage restoration contractor together with the affected parties, informs real estate agents and insurers, and thus manages precisely these communication and coordination efforts. At the same time, they collect data: root cause analyses, leak detection reports, measurement logs, drying service quotes, demolition and restoration quotes, and information on the parties involved which, due to data protection regulations, usually cannot be easily stored proactively, for example, in a condominium tenancy agreement.

This results in a dataset that is transmitted to the insurer. The problem is that, in practice, the claim report usually does not reach the insurer with all the necessary data included. The insurer must review the claim, reconcile the data, and then follow up later because master data is missing, information about the parties involved is missing, the leak location is missing, the quotes are inconsistent, or other details are missing. This delays the entire process, specifically on the insurer’s end. That’s exactly where we come in: We provide the insurer with the data in a structured format and with standardized data quality, so that they can make decisions in minutes rather than months, because all the information relevant to the decision is available.

How has the concept behind Vectoryx evolved since its founding, and what have you learned along the way that you wouldn’t have expected at the beginning?

That, at its core, this is not purely a software problem, but a translation problem. In the insurance industry, we have very long sales cycles. At the same time, appraisers and restoration contractors still perform much of their work manually. So we’re seeing completely different sectors of the economy coming together: the trades through the restoration specialists, the assessment of damage by the appraiser, the insurer with its traditionally highly regulated processes, and the property manager with his or her ever-increasing digitalization. Each of these stakeholders speaks its own language, and we need to bring them all to the table.

A good example is the skilled trades: They are extremely difficult to digitize, and others have already tried to do so. This usually fails not so much because of the technology, but because there is simply no need for it among tradespeople. We have addressed and systematically solved precisely this problem by bringing all stakeholders together in a single process without creating additional hurdles.

Vectoryx connects insurers, property managers, and renovation partners in a single system. Where exactly do the biggest information gaps occur today between these parties?

Basically, wherever there’s a system boundary. And these system boundaries are very diverse. The skilled trades operate differently, including on the software side, than the housing industry. The housing industry operates differently than the insurance sector. Wherever there’s a system gap, these very losses, and sometimes massive efforts to bridge them arise. But this applies just as much to the point at which the people affected are involved. Some are highly tech-savvy, while others for example, an 80-year-old, prefer to communicate exclusively by phone. So we’re dealing not only with system-related gaps but also with generational ones. In this regard, we also act in part as an impact startup, as we actively promote inclusion to the best of our ability and integrate all stakeholders as seamlessly as possible.

You rely on BiPRO and GDV compliance in Germany and their equivalents in Austria and Switzerland as a foundation. How important is regulatory interoperability for acceptance among established insurers, and in what ways does it tend to hinder innovation?

Our processes are modeled after BiPRO and the GDV standard, but we have deliberately chosen not to be fully bound by them. Instead, we focus on ensuring that these various standards are actually implemented in practice. This also includes FRIDA, the insurance industry initiative that aims to use a digital wallet to consolidate this very flow of information among the various parties involved. And, of course, this creates a point of friction, because the question arises as to what fundamental interest an insurer has in standardization. It might even make the insurer somewhat interchangeable as a result: If all information is based on the same system architecture, it becomes more difficult to demonstrate its own expertise. However, that is more of a question for the future.

For us, it really doesn’t matter which system the insurer uses in the first step, because we can integrate with any existing system environment. We can access the insurer’s system directly via APIs. However, we can also use agents to access the insurer’s systems at no additional cost and with very little implementation effort. That’s exactly what we like to do in pilot projects: connect directly to the system and provide information without the need for complex system integration upfront.

With the ChatGPT integration, you’re taking an unusual approach to initial reporting. Why use a consumer interface like ChatGPT instead of your own app or portal?

Of course, we also offer affected parties the option to access our claims process directly via a link or to be notified simply via traditional email. But we have to face reality: There are an insane number of apps out there today. The insurer has its app, the property management company has its app, and in some cases, even contractors now have their own apps. For someone dealing with a claim, it’s then almost impossible to keep track of where the case stands, what the next steps are, and what information is needed.

We could launch the next app right now and say, “Hey, sign up here to see the progress.” We’re deliberately choosing not to do that. Instead, it’s great to integrate into the systems people are already using. The user numbers for ChatGPT and similar systems show that these interfaces have caught on. That’s exactly what GPT integration is for: enabling users to file claims, update information, and check status updates right where they’re already working. And it allows us to do something else: claims can be reported to us even if there isn’t yet a contract with an insurer, a property management company, or a broker. At this level, we’re expanding in all directions.

How do you ensure that a dialog-based, AI-powered claims reporting system actually provides complete and reliable data, especially in cases of complex property damage?

By bringing all stakeholders together. We know exactly what information the various insurers need because we maintain the necessary partnerships. We have databases and integrations for example, to review quotes, and we involve appraisers. This allows us to respond immediately upon receipt of a claim, and that response involves directly engaging restoration contractors, appraisers, brokers, and other relevant parties. In this way, we compile the information right in the claim report so that it can then be transferred to the insurer easily and completely.

A native integration based on the OpenAI Apps SDK is in the works. How will this differ from the current GPT Store model?

We’ve actually already received approval for this, and the launch is coming up very soon. The difference is that users can incorporate our app directly into their daily workflows. They can access our integration from within an existing chat and initiate the claim right there. And they can use it in context: If someone is already chatting with ChatGPT for example, about rent reductions, standard processing times, or questions regarding utility bills, they can specify their specific claim and use our SDK to ask questions directly about it. It’s also becoming interesting from the perspective of the housing industry, since many of the applications there work via GPT interfaces: So if a property manager “talks” to GPT about a property “tomorrow,” the transition to our platform can happen right there, without having to switch systems at all. These store-based transitions may therefore also be possible in the future.

It’s all included in the final release, and there will be quite a bit more added over the next few months.

What does your business model look like? Who pays for Vectoryx, and how does this change as more and more process steps are automated?

Our model is funded, in part, by insurers, since they stand to benefit the most. Insurers are suddenly able to settle claims within minutes rather than months. At this level, we offer insurers the opportunity to save around one thousand euros in internal staffing costs per claim. And, more importantly, they increase policyholder satisfaction, thereby setting themselves apart in the market.

Automated, structured processing, and, above all, staying on top of things for the customer and proactively keeping them informed, is entirely our responsibility. So we don’t just react, we actively provide information. The key point is: We don’t deliver software that needs to be set up and maintained by someone else, as was the case with traditional SaaS models. We deliver a solution. And this solution benefits the insurer the most, because they derive the greatest value from it. Looking to the future, AI technologies will naturally continue to streamline processes, and as an AI-native, agent-based company, we’re strongly committed to this. However, it’s also important to note that every single insurance claim can be so complex and open up so many paths left, right, or straight through the middle, that we see ourselves more as a deep-tech company than as a traditional insurtech company.

Where do you see the greatest resistance from insurers and administrative bodies when it comes to implementing a cross-platform claims infrastructure?

For insurers, the main source of resistance is that they still view many processes such as claims intake as something they must handle themselves. So a shift in mindset and an openness to AI-native solutions are needed to improve their own service quality. This is a traditional issue in the industry that has developed over time: Insurers are large companies with lengthy decision-making processes, and their cycles are correspondingly long. It simply takes time to make inroads there.

However, we have some very exciting pilot projects in the pipeline that will allow us to handle corresponding volumes. On the other hand, we see virtually no resistance from property management companies, since our service is free of charge for property managers. We reduce their workload per claim by an average of about ten hours, spread over several months. We provide our property management clients with a dashboard, and we have clients for whom we’ve already saved hundreds of workdays in staff and system resources, time that can now be put to other uses.

By the way, our clients include not only traditional third-party property managers but also property owners and investors. And since we operate as a white-label solution, no one realizes that we’re working behind the scenes. We improve service quality and customer satisfaction on behalf of our clients. Consequently, there are no negative concerns about our services within the housing industry.

You are currently operating primarily in the DACH region. How scalable is your model across national regulatory boundaries?

We’re already active in the U.S. and have our own branch there. The fact is, “insurance claims” exist in every country in the world. There’s always a home insurance provider, and ultimately, there’s someone who has to repair the damage. The process we’re establishing here can therefore be replicated in any country certainly with one or two regulatory adjustments, but in principle, it’s always the same. This works in Germany, France, the UK, the U.S., and also in Asian markets. Accordingly, we’re currently planning a major expansion into other countries.

How much discretion do you give AI in the claims process, and where do you deliberately draw the line when it comes to human review?

As a rule, we do not make a settlement decision right away. Once a claim has gone through our quality pipeline which includes, among other things, verifying the cause, the insurance policy, and the terms of coverage we know with a very high degree of certainty whether the claim is covered or not. However, we do not settle the claim at this stage; the decision remains with the insurer.

However, we collaborate with certain insurers to the extent that we receive claims approval authority: If claims meet our quality criteria, we can approve high-frequency claims up to a defined approval limit from the outset, so that the insurer does not incur any additional effort afterward. In this way, we handle high-frequency claims, which in some cases account for up to sixty percent of all claims.

An important point in this context is the “zero-claim” report. When a claim first arises, no one knows whether it is even covered by insurance; that only becomes clear as the process unfolds and after the cause has been analyzed. Consequently, we process a large number of claims that ultimately turn out not to be covered. This helps us shed light on the “hidden claims” that currently exist among insurers: claims that aren’t covered, claims below the deductible that are never reported, or claims where a broker or administrator actively decides not to report them to avoid increasing their own loss ratio. The claim is effectively absorbed. The result is that accurate underwriting and risk calculation are actually impossible.

That’s exactly what we resolve, because we’re based at the property itself. It doesn’t matter to us whether there has been a change in insurer, broker, property manager, owner, or tenant in the past (or in the future), because we’re always based at the property. This enables us to provide historical master data and historical risk data, something no one else has been able to do until now, and to eliminate the blind spot in underwriting.

Ultimately, however, we also have employees who actively step in during more complex situations to clarify issues or make decisions.

What role does trust play for both policyholders and insurers when an AI system becomes the central interface for claims?

It’s important to understand that, as a rule, we initially only provide guidance. The actual decisions are made by the various authorities. What’s crucial is that we keep people engaged throughout the entire process. After all, in most cases, it’s not that someone necessarily wants to speak with a person. When an affected party calls the insurer, the restructuring firm, or the administrator, they usually want information because their need for information hasn’t been met. That’s exactly what we manage from the outset by keeping affected parties and every other stakeholder so well informed that it’s clear at all times when what is happening. The major advantage is that there are no longer any blind spots in these processes. And because these blind spots are eliminated, trust is significantly higher than in traditional claims processing, which requires a large allocation of personnel resources that are, by their very nature, susceptible to vacation, illness, and staffing shortages. We act as a highly reliable partner.

Where will Vectoryx be in three years? Will it remain a claims management tool, or will it evolve into something bigger such as a data infrastructure for the entire real estate and insurance industries?

We see ourselves in the future as the central data interface for insurance claims in the housing sector. Our core capability will always be claims management, specifically, AI-native and agent-based claims management. Of course, there are also opportunities to further develop the product. We have a vast amount of risk data and, as described, can provide reliable risk index data. We also have an enormous amount of data on restoration quotes, prices, and price trends. This enables us to provide comprehensive advice to both the insurance and real estate industries on how risks can be mitigated—for example, through maintenance measures. And this risk mitigation can be factored into insurance premiums. If an insurer lacks risk information, we can provide exactly that.

There’s another important point to consider: Homeowners insurance is currently extremely unattractive to many insurers. This is evident from the fact that many are withdrawing from the market; in each state, there are really only a handful of truly relevant homeowners insurers left. This is because claims are becoming increasingly expensive and the processing time is getting longer and longer. In the United States, the average turnaround time until the insurance company responds is over 55 days, and this figure is rising sharply. In Germany, too, this has been the subject of strong criticism for quite some time. With our service, we are able to restore building insurance to a level that is attractive to insurers and gives property owners, as policyholders, the assurance that their buildings are realistically insured—and, among other things, helps them reduce their own liability.

If you could give just one piece of advice to someone who reports water damage in their apartment tomorrow, what would it be?

Please be understanding of the typical processing times of the various parties involved. Although they usually already work together, they do not operate according to the kind of time standards that are generally expected these days.

At the same time, it must be honestly pointed out today that processing times at the insurer are very long. Neither the administrator nor the parties involved in the trades are to blame for this, these are processing times that affect the entire industry and are partly due to the fact that lawmakers impose requirements whose scope is often difficult to grasp.

We’re trying to clarify the situation here: These delays do exist in the market, and they place a burden on those affected. But times are changing, and there are certainly ways such as through our services to handle claims more effectively and significantly faster.

How do you handle data protection when an AI system aggregates claims data across multiple parties?

We naturally adhere to applicable data protection standards and provide our services exclusively within the framework of commissioned data processing. This means that the data subject, as well as all other stakeholders, are involved in such a way that each party receives from us only the data that is strictly necessary to fulfill the legitimate interests of the parties involved. Our approach is to store data only to the extent that it is actually necessary and, where appropriate, even to anonymize it during individual process steps. We expressly consider these standards to be sensible. Here, too, we provide the insurer with another major benefit: For example, we pre-filter data during the reporting process so that it poses no data protection risks. The details are where things get interesting, as there are indeed significant differences between, for example, the German and American markets, but that is a topic for another time.

Binci Heeb asked the questions.

Alexander Stade is the founder and CEO of Vectoryx. Before founding the company in 2025, he spent several years building his own property management firm specializing in condominium and rental property management. The inefficiencies he encountered in claims management during that time served as the inspiration for Vectoryx.

Through Vectoryx, he is currently developing an AI-native infrastructure that brings together insurers, property managers, insurance brokers, and renovation partners in a single process. The company is based in Hamburg and New York; in 2026, Stade and Vectoryx were selected for the Founders Program at the Global Insurance Accelerator in Des Moines.

See also: Swiss InsurTech Hub Looks Back on an Eventful First Half of the Year

He built and launched a credit marketplace platform regulated by FINMA, weathered the COVID crisis as a fintech founder, and is now dedicating himself to what first inspired him in his master’s thesis: the use of artificial intelligence in the financial services and insurance industries. Ghassen Benhadjsalah is an actuary and AI engineer, co-founder of inncivio, and one of the leading figures at the intersection of mathematics, technology, and insurance risk. thebrokernews sat down with him for the platform’s first interview with an actuary.

Actuaries are the unseen architects of the insurance industry. They calculate the costs of risks, the probability of claims occurring, and how much capital an insurer must set aside to remain solvent in the future. Ghassen Benhadjsalah is one of them, but he’s not the type to sit quietly in the background doing calculations. His career path took him from the University of Lausanne through several startups to his current position at inncivio, where he works with Anna Raafat and Fernando Felix to integrate real-time risk assessment using AI into insurance and trading platforms.

For thebrokernews, this is a good opportunity to learn more about his perspective on an industry in transition. For me, these two disciplines have always been closely linked. Both are quantitative fields, and in both, you have to make decisions within certain constraints. An engineer must think, design, and engineer within limited resources. That’s what defines an engineer: you rarely have unlimited time, an unlimited budget, or perfect conditions. You have to understand the problem, design a solution, and ensure that it works in the real world.

Mr. Benhadjsalah, you are an actuary and an engineer. That’s a rare combination. How did that come about, and what fascinated you about actuarial science?

An actuary faces a similar challenge, but in the context of risks. One must quantify uncertainties based on limited amounts of data. One never has a perfect picture of the future, yet must still make responsible decisions: How likely is an event, how severe could it be, how much capital is needed, and how should the risk be assessed?

This combination fascinated me. Engineering gave me the tools to develop systems, while actuarial science provided the framework for understanding uncertainties and financial consequences. Most of my career has revolved around bringing these two worlds together. Back then, we didn’t use the term “AI” in the way it’s understood today. We tended to talk about machine learning, statistical learning, and predictive models. But the core idea was the same: Can we use data to identify patterns that a human would have a hard time recognizing?

Your master’s thesis, which you wrote over 15 years ago, focused on the use of AI for fraud detection in the insurance industry. What motivated you to write it back then, and weren’t you a little ahead of your time?

Fraud detection in the insurance industry was an obvious topic, since insurance has always been a data-driven business. Claims, customer profiles, historical behavior, anomalies, and relationship networks, all of these contain signals. The challenge is that fraud isn’t always obvious. Often, it manifests as a subtle pattern across many variables rather than a clear warning sign. I was interested in whether algorithms could help insurers identify suspicious cases more quickly and accurately, not to replace human judgment, but to set priorities for where human expertise should be focused.

Looking back, it was probably still too early. But to me, it was obvious that financial services and insurance would eventually become much more data-driven. The technology wasn’t as advanced as it is today, but the direction was already clear. I’d say an actuary helps the market assign a price and a structure to uncertainties.

Actuaries are often regarded as the unsung architects of risk. How would you explain to a layperson what an actuary actually does?

Insurance exists because individuals and businesses are exposed to risks they cannot bear on their own: illness, accidents, longevity, natural disasters, business interruptions, and much more. The job of an actuary is to estimate how often these events might occur, how severe they might be, and how much money needs to be collected or set aside to ensure that promises can continue to be kept in the future.

Simply put: An actuary translates uncertainty into numbers that enable companies to make responsible decisions. It’s not just about insurance pricing. It’s also about solvency, capital, fairness, long-term sustainability, and trust. The best actuaries are not just mathematicians. They understand human behavior, regulation, business models, and the limitations of models. This last point is very important: A good actuary knows that the model is just a tool, not the truth. The biggest challenge was making a credit decision in real time while still managing risk responsibly. In a “buy now, pay later” environment, the customer expects an immediate response. The merchant wants a seamless payment process. But the company still has to decide whether it is taking on an acceptable credit risk with this transaction. A process that takes hours or days is not feasible. The decision must be made immediately.

They joined Swissbilling, now known as Cembra Pay, and we built the AI model for real-time credit risk assessment there. What was the biggest challenge in that process?

That was a perfect early application of AI. Ultimately, building an AI model is about predicting outcomes using a mathematical approach. In this case, the event we modeled was non-payment: the end customer receives the goods but is subsequently unable or unwilling to pay the invoice.

What made it interesting was that the industry already had access to extensive data. We were able to access a wealth of information in real time and use it to make better decisions. Back then, many companies were still relying on decision trees, static rules, and relatively rigid models. We were already considering machine learning as a way to assess risks more dynamically.

The other challenge was that the model didn’t exist in a spreadsheet. It had to work in a real company with real customers, real merchants, real losses, and real pressure to grow. That’s where my background in engineering came in handy. It’s one thing to design a model; it’s another to make it work in practice. A traditional fintech founder often starts with the product, the market, and the growth opportunities. An actuary also considers these aspects, but immediately asks: Where is the hidden risk? What happens during an economic downturn? What assumptions are we making? Are the incentives aligned? What happens if the model is wrong?

You then founded your own credit marketplace platform called Acredius, which is regulated by FINMA through the SRO PolyReg. How does the mindset of an actuary differ from that of a traditional fintech founder?

At Acredius, we connected investors with small and medium-sized businesses that were seeking financing. This meant that we didn’t just build a marketplace; we also had to deal with credit risks, investor expectations, regulatory obligations, and trust.

An actuary’s way of thinking has helped me to look beyond just volume. In the lending business, growth can be dangerous if the risk isn’t properly understood. A marketplace can appear very successful in good times because loans are being issued, but the real test comes later, when repayments are made, or not.

So I would say that an actuary’s way of thinking requires a certain amount of discipline. It doesn’t take away your ambition, but it does force you to consider the other side of the coin. COVID was a very humbling experience. You can model credit risks, probabilities of default, macroeconomic scenarios, and stress cases. But sometimes the shock isn’t just economic in nature, it’s also political and structural. In Switzerland, the market changed almost overnight when the government suddenly offered interest-free loans to businesses.

COVID hit Acredius hard, as the government suddenly began offering interest-free loans. As an entrepreneur and risk specialist, how do you deal with a risk that you simply couldn’t model?

For a lending marketplace, this is not a typical competitive development. It changes the entire demand side of the business. Why would a company take out loans through a marketplace when it can obtain government-backed financing for free?

As a risk specialist, the lesson here is that not every risk can be modeled using historical data. Some risks involve regime shifts. The world in which you trained your model is no longer the world in which you operate.

As an entrepreneur, you have to accept reality quickly. You can’t get too attached to your business model or your original plan. You have to ask yourself: What still holds true, what has changed permanently, and what can we do with the resources and knowledge we have at our disposal?

It was painful, but it also reinforced one of my core beliefs: Risk is not the enemy. Risk is the raw material. The danger lies in pretending that risks don’t exist. inncivio is an agent-based revenue infrastructure for financial services and insurance. We help platforms increase their transaction volume by hyper-personalizing the transaction experience for each individual user.

Today, you are a co-founder of inncivio, where you integrate AI and machine learning directly into insurance and trading platforms. What exactly does the company do?

If you look at the last 15 to 20 years, access to financial products has increased dramatically. Today, we have more digital banking products, more trading products, more types of derivatives, more insurance products, more digital assets, and even prediction markets. The number and complexity of financial products have skyrocketed. Yet the way users interact with these products has remained relatively rigid, standardized, and complex. Most platforms still present the same workflow, the same screens, and the same explanations to very different users, even though these users have varying levels of knowledge, different intentions, and different moments of confusion.

This is exactly the problem that inncivio solves. We believe that financial products are increasingly being developed in a “raw” form at the product level, and that platforms then need an intelligent layer to tailor the transaction experience to each individual user. We provide that layer.

We integrate directly into financial platforms via a lean infrastructure layer. We collect anonymized behavioral and contextual signals (for example, where the user hesitates, which step they’re on, which product they’re viewing) and then provide context-based assistance such as explanations, prompts, tooltips, videos, or calls to action.

The goal is not to manipulate the user. The goal is to reduce friction and improve understanding at the very moment it matters most. For the platform, this can increase transaction volume and user retention. For the user, it can help make complex financial processes easier to navigate. It’s important that we’re directly integrated into the execution path. We’re not a generic chatbot outside the product. We’re embedded in the actual decision-making process. Yes, and I think this conflict is healthy.

At inncivio, you serve as both an actuary and an AI strategist. Are there times when these two perspectives come into conflict?

The AI strategist wants to move quickly, test, learn, and optimize. The actuary asks: What are the assumptions, what are the unintended consequences, what happens if the model is wrong, and how do we measure the impact accurately?

In the financial services sector, optimization cannot be viewed as a purely technical task. If an AI system influences user behavior, it is essential to understand what it is optimizing for. Does it help the user? Does it improve the platform’s profitability? Is it compliant? Is it sufficiently explainable? Could it lead to distortions?

So the actuary in me ensures discipline, and the AI strategist in me ensures speed and a willingness to experiment. The key lies in combining the two. AI without risk discipline can be dangerous. Risk discipline without innovation can become irrelevant. The role has changed significantly. Ten years ago, many actuarial models were still relatively traditional: statistical models, reserve methods, rate tables, and capital models. These are still important, but the data landscape has changed dramatically.

How has the role of the actuary changed over the past ten years as a result of AI and machine learning, and where is the field headed?

Today, actuaries have access to more detailed data, more real-time signals, and more powerful modeling techniques. Machine learning can identify patterns that traditional methods might miss. Generative AI can assist with documentation, scenario analysis, customer communication, and internal workflows.

However, I don’t believe that AI diminishes the importance of actuaries. I think it’s changing what makes an actuary valuable. The actuary of the future must be more technically savvy and product-oriented, and feel more comfortable working with data scientists and engineers. Yet the actuary’s core competency remains indispensable: an understanding of uncertainty, incentives, long-term commitments, and the consequences of poor decisions. In a world where AI can provide answers very quickly, the role of the actuary will increasingly consist of questioning whether the answer is reliable, fair, robust, and economically sound. Technically, a great deal is possible today, but the term “real-time risk assessment” is sometimes used too loosely.

There is a lot of talk in the insurance industry about real-time risk assessment. What is actually technically possible today, and what is still just marketing jargon?

It is possible to use real-time or near-real-time data to improve decision-making. In the insurance industry, this could involve dynamic underwriting, fraud detection, claims triage, price adjustments, risk prevention, or customer counseling. In retail or the lending business, this can mean identifying hesitation, risk tolerance, suitability issues, or behavioral patterns as soon as they arise.

Technically speaking, the tools are available: APIs, event streams, machine learning models, cloud infrastructure, and embedded AI interfaces. The biggest challenges are generally not the algorithms themselves. Rather, they are data quality, regulatory requirements, explainability, integration with legacy systems, and organizational readiness.

Marketing jargon is the notion that every risk can be perfectly assessed in real time. Some risks develop slowly. Some require context that isn’t available digitally. Some cannot be predicted based on past behavior. And some real-time signals are noisy or misleading.

So I would draw the line this way: Real-time risk assessment is valuable when it improves a specific decision within a specific workflow. It becomes jargon when it’s portrayed as some magical level that instantly understands all risks. I think both are true.

Swiss InsurTechs often struggle on the international stage. Do you agree, or is the Swiss market underestimated?

Swiss InsurTechs and FinTechs often find it more difficult to scale internationally because Switzerland is a relatively small market. While it is possible to develop a strong product, a company may not achieve the same scale domestically as one that launches in the U.S. Raising capital can also be more conservative, and Swiss companies sometimes market themselves less aggressively than their international competitors.

At the same time, the Swiss market is underestimated. Switzerland possesses in-depth expertise in the fields of insurance, banking, risk management, regulation, and asset management, as well as technical know-how. If you develop something that works in Switzerland – especially in a regulated environment – it can be a strong sign of credibility.

The challenge is that Swiss companies need to start thinking globally sooner. The quality is often there. Their ambition and communication must match that level. I think one of the biggest risks is that the industry is not preparing its infrastructure for modern AI.

As someone who has been working at the intersection of insurance, AI, and technology for years, what risks do you think the industry still fails to recognize?

The insurance industry has very extensive data sets, but often a very inadequate data infrastructure. The way data is collected, structured, cleaned, linked, and made usable for AI and machine learning is still lagging behind in many organizations. This is not just a technical problem. A shift in mindset is required.

The risk is that companies will apply a generic AI model to poor-quality data and expect transformative results. The results will then be generic, incomplete, or disappointing. People will look at this and say, “AI isn’t ready for our industry yet,” even though the problem isn’t just with the model. The problem is the underlying infrastructure. This is dangerous because it can cause the industry to fall even further behind. AI needs context, structure, and high-quality data to be useful. Without these prerequisites, even the best model will struggle.

That’s why people with a dual background are becoming increasingly valuable: people who are knowledgeable about data engineering and technology, but also about insurance, finance, and risk. You have to be able to view technology through the eyes of an actuary and the insurance industry through the eyes of a technologist. This is where much of the added value is created. My advice to you is this: Master the fundamentals thoroughly, but don’t stop there. You still need knowledge of mathematics, probability theory, statistics, finance, and insurance. Generative AI does not replace this foundation. In fact, it makes this foundation even more important, because you need to be able to recognize when the machine is wrong.

What advice would you give to young people who are pursuing a career in actuarial science today, especially in light of generative AI?

I would also encourage young actuaries to explore programming, data science, machine learning, and product thinking. Don’t just see yourselves as people who create reports or models. See yourselves as people who can help build decision-making systems.

The best opportunities will go to those who can build bridges between different worlds: actuarial science and AI, regulation and innovation, business and technology. My recommendation is also this: Stay curious. AI will transform many tools, but curiosity, good judgment, and a sense of responsibility will continue to be highly valuable. Passion, without a doubt, and more specifically, a passion for creativity.

Finally: You have co-founded or founded six companies. What drives you: risk or passion?

I enjoy building things. I like the feeling of taking something from zero to one: from an idea, through a first version, to a product, to a company, to something that customers actually use. That’s what brings me the greatest joy.

I also like taking my fate into my own hands. Entrepreneurship is difficult and sometimes painful, but it gives you the opportunity to shape your own path and create something that reflects your own view of the world.

I’m not drawn to risk just for the sake of risk. I don’t believe entrepreneurs should glorify uncertainty. But I’m drawn to problems where uncertainty prevails and where technology can help people make better decisions. That has been the common thread throughout my career: insurance fraud, credit risk, lending, trading, financial consulting, and AI. These are all areas where decisions are complex and involve real risks.

So I would say that it’s not risk that drives me. Risk is the material I work with. It’s the creative process that drives me.

Binci Heeb asked the questions.

Ghassen Benhadjsalah is an experienced tech entrepreneur with an academic background in AI and actuarial science. Over the years, he has founded several startups, most of which operate at the intersection of fintech, insurance, and AI. He is a passionate developer and a tenacious, resilient founder who possesses both technical and business expertise.

See also: How inncivio Uses AI to Redesign Context-Aware User Guidance for the Transactions Industry

Artificial intelligence has revolutionized document processing. While many providers only entered this field with the advent of large language models, Parashift has been working on automated document processing since 2018 and has built up a large customer base since then, including AXA, Swica, Raiffeisen, Swisscom, Mercedes, DB, and Glencore.

During this time, the Swiss company has built up a broad customer base in the insurance and banking sectors, as well as in the healthcare industry. With the EU AI Act and the current debate on “digital sovereignty,” a new topic is now taking center stage, one that affects all regulated industries. In an interview with thebrokernews, Parashift Founder and CEO Alain Veuve explains what Swiss and European companies need to know right now, how he views the development of AI-powered document processing, and why he believes that regulation is not necessarily an obstacle but can also be a competitive advantage.

Mr. Veuve, you founded Parashift eight years ago, long before Large Language Models (LLMs) brought artificial intelligence (AI) into the mainstream. What motivated you back then to specialize in automated document processing? And what fundamental technological changes have taken place since then?

It actually started out as a pragmatic need: at the time, we needed a solution ourselves for the automatic extraction of data from many different documents. My initial inquiries with established providers were quite sobering – not because they said it couldn’t be done, but because their definition of “solved” and mine were fundamentally at odds. Technological advancements since then have been significant. The rise of large-scale language models has massively accelerated public interest in AI, and that has also spurred a lot of activity here at Parashift. But the greater the hype, the clearer it became: for regulated, high-volume document workflows, we don’t need general-purpose solutions, but rather specialized systems that operate reliably and with verifiable accuracy. That was our conviction from the very beginning.

Parashift serves clients in the insurance and banking industries, among others. What do these industries have in common when it comes to document processing, and where do their requirements differ the most?

What they have in common is volume, variance, and compliance pressure. Both industries process immense volumes of unstructured documents in regulated environments, where every decision must be traceable. The difference lies primarily in the regulatory context. Insurance companies work with particularly variable and sensitive document types such as medical reports, accident reports, and medical expert opinions, which places high demands on AI. In banking, it is primarily credit decisions and KYC processes that are explicitly classified as high-risk under the EU AI Act. And with DORA, an additional binding EU regulation is already in effect that mandates operational resilience against ICT risks, including dependence on third-party providers.

You speak daily with leading insurance companies and banks in Switzerland and Europe. What insight has surprised you the most in recent months?

The gap between what companies believe about their AI positioning and the reality of their architecture is wider than I would have expected. I repeatedly speak with IT and compliance managers at large, regulated companies who sincerely believe: “We host on EU servers, so we’re protected.” They’re not. The U.S. CLOUD Act requires U.S. providers to hand over data upon government request, regardless of the server’s location. Sovereign AI and data are, first and foremost, an infrastructure decision. I’ve also been surprised lately by the vehemence with which European decision-makers have put the issue of sovereignty on their agenda. In the first half of the year, we acquired customers – for example, in the banking sector – for whom sovereignty was the number one criterion. When the latest, leading document intelligence and sovereignty are both required, there are almost no providers in Europe.

For many readers, the EU AI Act is still an abstract topic. Could you briefly explain what it actually regulates and which deadlines are particularly relevant right now for companies in the insurance and banking sectors?

The EU AI Act is the world’s first horizontal AI regulation. Its fundamental principle is risk-based: the greater the potential harm to people, the stricter the requirements. Of particular relevance to insurance companies and banks is the so-called high-risk category as defined in Annex III: AI used in creditworthiness assessments and in risk assessments for life and health insurance. Any entity that uses AI-supported document processing in these areas is fully affected. Even though the implementation of the regulations has been repeatedly delayed, the general direction remains clear. The requirements for conformity assessments, technical documentation, risk management systems, logging obligations, and human oversight mechanisms will become mandatory. Those who fail to comply can expect fines in the millions.

Switzerland is not a member of the EU, but experience has shown that it adopts EU regulations after a certain delay. What does the EU AI Act specifically mean for Swiss insurers and banks that also operate within the EU?

People often confuse two issues here that must be addressed separately. First, extraterritorial effect: the EU AI Act applies to anyone whose AI output is used within the EU, regardless of where the company is headquartered. A Swiss insurer that serves customers in Germany or Austria is already within the scope of the Act for these activities. Second, the issue of Swiss regulation: FINMA is clearly moving toward the principles of the EU AI Act. Anyone building EU-compliant infrastructure today is investing in a strategy that will remain relevant regardless of how the Swiss regulatory calendar unfolds. I recommend treating compliance not as a checklist, but as a competency to be developed.

Under the EU AI Act, what risk category applies to the use of AI-powered document processing in the insurance industry – for example, in automated claims assessments or risk evaluations – and what obligations does this entail for users?

Risk assessments in underwriting – such as for life and health insurance – fall under Annex III of the EU AI Act and are therefore classified as high-risk. In claims processing, the exact classification depends on the specific use case: as soon as AI outputs are incorporated into decisions that directly affect individuals, careful classification is mandatory. The obligations for deployers – that is, the insurers that operate such systems – are substantial in every case: conformity assessment, technical documentation, a complete audit trail, transparent outputs, and verifiable human oversight. This last point is systematically underestimated. Article 14 of the EU AI Act does not require nominal oversight, but rather operational oversight: the “reviewer” needs the tools and expertise to actually override an AI decision, not just rubber-stamp it. And these obligations lie with the deployer, not the model provider. Our Document Intelligence Platform offers precisely these “AI Guardrails,” even when third-party models are used.

Many companies view regulation primarily as a burden. You, on the other hand, argue that the EU AI Act can also be an opportunity. What exactly do you mean by that?

No one in the business world loves regulation. One can debate the purpose and intent of the EU AI Act. From the perspective of an AI entrepreneur, I would of course want as much data as possible to be freely available. From my perspective as a citizen, however, I see things a little differently. There’s not much point in dwelling on it, because at the end of the day, the law is a reality, and as a company, you can use it to operate as competitively as possible in the market. At Parashift, we specialize in building precisely this bridge between the latest, innovative document AI and sovereignty and compliance.

What advice would you give to a medium-sized insurer or broker that does not yet have a clear compliance strategy for the EU AI Act? Where should they start, specifically?

Start with the use case inventory, not the technology assessment. The most common mistake is that people ask, “What AI systems do we have?” before asking, “What decisions do we make using AI, and who does that affect?” Next, conduct an honest assessment against Annex III. Any organization that has document processing workflows feeding into claims decisions or creditworthiness assessments is considered high-risk. Next, evaluate your current AI provider against the deployer’s obligations. Can they provide complete compliance documentation for high-risk systems? If this turns into a lengthy consulting exercise, that in itself is a bad sign.

The term “digital sovereignty” is currently the subject of much discussion, but it often remains vague. What does it specifically mean for the risk industry, and why is it so relevant right now?

Digital sovereignty is not just another marketing term. In operational terms, it means: can I provide a definitive answer during an audit as to where my data flows during processing, who can access it and under what legal basis, and whether this is compatible with my regulatory obligations? If the answer is “I think it’s my U.S. provider’s EU server instance,” that isn’t necessarily a satisfactory response. The U.S. CLOUD Act creates a loophole here that cannot be closed by hosting geography. For the Swiss financial sector, this is nothing new in itself: maintaining control over data for critical functions outsourced to third parties has long been a requirement of FINMA. What is new is the context: AI systems today process data more sensitively and quickly than any previous technology, and the regulatory infrastructure is just catching up. This suddenly turns a familiar expectation into an urgent operational issue.

Where do you see the greatest dependencies of European insurers and banks on non-European AI providers? And what risks do these dependencies pose, for example, in terms of data protection or regulatory requirements?

In my opinion, the greatest dependency lies in the model layer. The dominant language models, on which many enterprise AI deployments are based, come from the U.S. – meaning they are hosted in the U.S. and are therefore subject to U.S. law. This applies regardless of whether the physical server is located in Zurich, Frankfurt, or Dublin. What often receives even less attention is operational dependency. If a regulated workflow relies on a third-party LLM API, you are dependent on that provider’s decisions regarding pricing, availability, and versioning. We’ve seen providers discontinue models or adjust prices in ways that make already-running deployments unprofitable. For regulated companies that need stability and predictability, this poses a serious operational risk.

Parashift positions itself as a leading European alternative in intelligent document processing. What role does the company’s Swiss or European location play in building trust with your customers, particularly in security-critical industries such as insurance and banking?

Location matters, but more for concrete reasons than for symbolic ones. Our infrastructure is 100 percent hosted in Switzerland, Germany, and the EU. We have no U.S. parent company and no support infrastructure in third countries, which clearly sets us apart from other providers. A second important point is regulatory compliance. We operate under the same framework as our clients. When we prepare compliance documentation, we always do so in accordance with FINMA and BaFin requirements.

With the advent of LLMs, document processing has changed significantly. How does Parashift’s approach differ from traditional LLM-based solutions? And in your view, what are the limitations of pure LLM approaches in security-critical applications?

The fundamental difference is that we develop what are known as “Special Purpose Small Vision-Language Models,” not generic, large LLMs. LLMs have a very broad scope, which works very well for general tasks. But when it comes to complex, regulated enterprise document processing, this is where the challenge lies: they sometimes hallucinate and are inaccurate, which is unacceptable in a regulated corporate environment. Then there is the issue of transparency. Article 13 of the EU AI Act requires traceable outputs. A model that reads a value without specifying exactly where in the document and with what level of confidence cannot provide regulatory evidence of this. We not only provide the model’s result but, with our platform, also ensure full traceability of every single step in the process.

How has the adoption of AI-powered document processing among insurers evolved in recent years, from initial pilot projects to widespread production use?

AI-powered document processing has been around for a long time – long before AI became a trend and everyone was talking about it. We launched the world’s first no-code, cloud-first, AI-first platform for processing a versatile document catalog, and this type of setup became the standard. Not that I want to give the impression that I think this was all because of us – I think it was simply the logical evolution of the field, which had long been known as “capturing.” While this journey began with extracting information from documents, today it’s much more about understanding document content holistically and using that to generate automation for downstream processes. We’re also seeing strong growth in use cases where AI/LLM-ready document data is produced. I believe that in the future, every document that enters a company should go directly into a kind of data lake as Markdown. This data is essential for the development of company-specific AI applications.

Which development in AI and document processing will have the greatest impact on the insurance and financial sectors over the next two to three years?

What I see happening in the coming years is a market correction, but not a revolution – rather a return to reality. The first wave was: “We’ll take whatever’s readily available,” and those were mostly the major platforms of the tech giants. What we’re now observing is a second trend: companies in regulated industries are realizing that generic solutions don’t deliver what they promised in their specific context. The questions being asked today in the procurement process – namely auditability, data sovereignty, and deterministic outputs – are precisely the issues for which specialized systems were built. This shift will accelerate significantly over the next two to three years.

If you could give European insurers one piece of advice on how to deal with AI regulation and technological change, what would it be?

Stop treating compliance and performance as conflicting goals – that’s the wrong way to frame the issue. The characteristics that make a system compliant with regulations – namely traceability, reliability, and architectural control – are the same ones that make it stable and trustworthy in production operations. This is no coincidence; it is due to the architecture. Companies that understood this early on are now building a significant lead.

The questions were asked by Binci Heeb.

Alain Veuve is an entrepreneur, thought leader, and expert in technological change with a focus on startups. Since 2016, he and his teams have been working on solutions based on artificial intelligence.

His ventures have included the accounting startup Accounto AG, the PropTech startup Fairwalter AG, and TYPO3 GmbH in Düsseldorf. Currently, as CEO and founder of the AI scale-up Parashift AG, he is overseeing the company’s expansion.

Over the past 20 years, Alain Veuve has been involved in a number of startups, both financially and operationally. As part of these activities, he has supported and provided strategic advice to various international companies in their digital transformation and e-business initiatives.

Today, Alain Veuve is a widely cited thought leader on digital transformation in Europe who regularly speaks at conferences. In recent years, he has given over 300 presentations. His blog, alainveuve.com, is a popular source of insights for decision-makers in the technology sector.

In 2017, Alain Veuve was named one of the Top 20 Voices in Europe by LinkedIn. XING also named Alain one of its “Top Writers” in 2017. He writes regularly for various publications.

See also: EU AI Act: Why Companies Need to Act Now

Fulvio Maccarone has spent over 12 years inside financial institutions and family offices and as a board director and restructuring specialist. His core conviction: the real problem behind failed investments is almost never a financial one. It’s a governance problem. Today he researches cognitive biases in boardroom decision-making and advises at the intersection

Fulvio Maccarone has built an unusual career not because he understands financial markets better than most, but because he recognised early on where the real problems lie. After roles at institutional investors and family office, he now works as a private markets investor’s advisor and independent Non-Executive Director. He also teaches governance to Swiss executives. In this conversation, he explains why intelligent, experienced people keep making the same avoidable mistakes — and what can actually be done about it.

Fulvio, you write that most investment problems are really governance problems in disguise. Was there a specific moment in your career when that became undeniably clear to you?

There was a specific moment, and I can point to it precisely because I lived through it. I was involved with a luxury company that had everything that should have worked. A renowned CEO with a strong public profile, a credible board, professional due diligence, and institutional investors who knew the sector. The first Series A round closed on what looked like solid foundations. Six months later, the CEO stood up at the first board meeting after the capital raise and announced that the Chinese and Russian clients had essentially disappeared overnight, the end of China’s gifting policy and the rouble collapse had wiped out the revenue base in one stroke.

What followed was the discovery that the capital hadn’t been used as planned, that the product had serious technical issues nobody had flagged, and that the markets the business plan had promised to open, the US, Japan, had never been touched. We replaced the CEO on the spot. Then came a second board meeting with a second round of bad news. Then a convertible. Then another. Then series B, C, D, E eventually 60 investors, 250 lines on the cap table, convertibles drafted on different templates, some not even countersigned. The company burned tens of millions trying to look attractive to a buyer who never came, in a market too small and too expensive to ever have justified the structure built around it. It ended in bankruptcy. Every investor lost everything.

What stays with me is not the external shocks, those were real but survivable. It’s that at every board meeting, with intelligent experienced people in the room, the decisions kept deferring the honest conversation. Nobody wanted to be the one to say the model was broken from the start. The governance failed not dramatically but incrementally, one deferred question at a time. That’s when I understood that the most dangerous boardroom failures don’t announce themselves, they accumulate quietly until there’s nothing left to save.

Your path has taken you from Pictet and BlueCrest Capital to family offices and board mandates. What fundamentally changed when you stopped advising from the outside and started getting involved operationally?

The question itself contains the answer, in a way. When you advise from the outside, you are always, at some level, protected. You bring your analysis, you make your recommendations, you present your views with conviction, and then you go home. If the client doesn’t follow the advice, or follows it badly, or the situation evolves in a direction you didn’t anticipate, there’s a professional distance that absorbs the impact. You learn, you adjust, but you don’t bear the full weight of it.

When I moved into operational roles, that distance disappeared. And what replaced it was something I hadn’t expected: a much sharper instinct for what actually mattered versus what merely looked important from the outside. In my previous roles, I had developed strong analytical frameworks. I knew how to read a situation, how to build a view, how to stress-test assumptions. What I didn’t fully appreciate until I was inside is how much of what drives outcomes has nothing to do with the quality of the analysis. It has to do with trust between people, with the unspoken rules about who can say what to whom, with the gap between what gets decided in a meeting and what actually happens afterward.

You can’t see that from the outside. You have to be in it, with your name attached to the outcome, before you really understand what you’re dealing with. That shift, from analyst to participant, is the most formative thing that happened to my professional judgment.

You operate at the intersection of capital, operations, and governance, three things most people keep separate. How do you explain to a traditional finance professional why that separation is a mistake?

I tell them to stop thinking about it as three disciplines and start thinking about it as one system with three pressure points. Pull on any one of them and the other two will move, whether you planned for it or not.

I was involved with a company where this played out as clearly as I’ve ever seen it. The lead investor structured a waterfall at Series A that was entirely defensible as a capital decision. Good downside protection, guaranteed hurdle rate, board seat. Textbook. What that waterfall then did, operationally, was drain the motivation of the founders the moment things got difficult, because the maths of the distribution meant they were effectively working for the investor’s return before they’d see a franc themselves. And what it did to governance was subtler but just as damaging; the board dynamic shifted from collective problem-solving to adversarial positioning, because the investor’s interests and everyone else’s were now structurally misaligned.

When the business hit external shocks, the board should have been able to have one honest conversation about what the company actually was and whether it was salvageable. Instead, it had a room full of people managing their own exposure. The governance structure that had become completely unworkable. Not because anyone made a single catastrophic decision, but because every capital decision had been made without asking what it would do to the people running the company or early investors. A traditional finance professional will tell you those are separate questions. They aren’t !

What is the biggest misconception board members have about their own role?

That they are there to validate. It’s rarely stated that explicitly, but it’s what the behaviour reveals. You see it in how board members prepare or don’t. You see it in how meetings are run, with management presenting and the board listening and nodding and asking the kind of questions that signal engagement without actually creating any friction.

And you see it most clearly in the moments that matter, when a decision is on the table that deserves to be challenged, and the challenge doesn’t come. I sat on a board where the chairman was also the founder, deeply respected, with a track record that made dissent feel almost impolite. Every major decision went through smoothly. Not because the decisions were always right but because the room had unconsciously agreed that his judgment was the ceiling and everyone else’s role was to help him execute it. That’s not a board. That’s an expensive advisory committee with legal liability.

The role I’ve come to believe in, and try to practice, is closer to what I’d call structured scepticism. Not opposition for its own sake, but a genuine commitment to asking the question that the room is trying to avoid. In a situation I lived through, the question nobody asked at the right moment was simple: is this market actually big enough to support the structure we are building around it? It wasn’t a complicated question. It didn’t require specialist knowledge. It just required someone to be willing to make the room uncomfortable. Nobody was. And that silence cost every investor at the table everything they had put in.

You train future board members. What capability is most often missing in candidates who look qualified on paper?

The ability to stay in the discomfort. That’s the thing you can’t see on a CV and can’t fake for long in a real boardroom. I’ve worked with board members who had everything on paper; the right credentials, the right experience, the right sector knowledge. They could analyse a situation, they could articulate a view, they could hold their own in a conversation. And then you put them in a room where the dynamic is difficult, where the founder is defensive, where the numbers don’t add up but nobody is saying it out loud, and they fold.

Often not dramatically, they don’t storm out or lose their temper. They just quietly find a way to not be the person who names what everyone can see. They ask a softer version of the question. They accept an incomplete answer. They tell themselves they’ll come back to it next quarter. I watched this happen repeatedly. The discomfort of confronting a charismatic CEO, of questioning a business plan that the chairman had championed, of being the voice that slows things down when everyone else wants to believe the next quarter will be different, that discomfort often is too much.

What I try to teach, and what I look for, is the capacity to sit with that discomfort and speak anyway. Not aggressively, not self-righteously, but clearly and without retreat. It is genuinely the rarest thing in a boardroom. And in my experience, its absence is more dangerous than any gap in technical knowledge.

You describe structures that “look solid on paper but collapse under pressure.” What are the early warning signs, things an outsider can actually spot before the collapse happens?

I watched every one of them appear in sequence in a situation I was directly involved in. The first sign was the gap between the story and the detail. The business plan was compelling at the headline level – new markets, strong brand, credible CEO – but when you pushed on the specifics, the US and Japanese market development that was central to the growth thesis had no concrete plan behind it. It was aspiration dressed as strategy.

The second sign was the CEO’s relationship with reality. He was genuinely charismatic, genuinely visionary, and genuinely more interested in the image of the company than in its operating fundamentals. That combination, charm over substance at the top, is one of the most reliable warning signs I know. The third was what I’d call selective transparency. Information flowed freely when it was good news. But when revenues started softening, nobody volunteered. You had to ask and then ask again. A board that is only informed of problems when they become unavoidable has already lost the ability to govern.

The fourth was the excessive use of expense capitalisation on the balance sheet, a flag that was visible at due diligence and quietly set aside. In my experience, when a company is generous in how it defines what counts as an asset, it is sometimes telling you something about how it manages inconvenient truths more broadly. None of these signs required inside information. They were all visible to anyone willing to look past the surface. The problem is that by the time a deal reaches the investment stage, there are powerful forces, momentum, social pressure, sunk time, the fear of missing out, that make looking past the surface feel like bad manners. Learning to do it anyway, politely but persistently, is one of the most valuable things I know.

Your research focuses on cognitive biases in board-level decision-making. Which bias do you consider the most dangerous, and why is it so rarely recognised in the room where it’s happening?

The one I keep coming back to is what I’d call expertise-driven overconfidence. Going back to my earlier example in the luxury goods, around that board table we had people who genuinely knew the sector, who had seen market cycles, who had built and sold companies. Real experience, real track records. And that collective credibility became the problem.

When the CEO presented a business plan that assumed rapid penetration of the US and Japanese markets, nobody pushed back hard enough, because everyone in the room had pattern-matched it to something they’d seen work before. When the R&D capitalisation raised a flag at due diligence, it was noted and moved past, because experienced investors had seen aggressive accounting before and the company had survived it. When the first signs of revenue concentration appeared, they were explained away, because people who have navigated emerging markets know that macro shocks happen and companies recover. Every time the room should have stopped and asked a harder question, experience provided a reason not to.

That’s the bias. It doesn’t feel like complacency. It feels like judgment. It looks like the calm of people who have been here before. The reason it’s almost never recognised in the room is that the people exhibiting it are the most credible voices at the table. Challenging it feels like challenging their track record, or being disrespectful, which nobody wants to do. In that company, by the time the first board meeting after the capital raise revealed the true situation, the pattern had already set. Experienced people had collectively talked themselves into a story, and the story had become more real to them than the numbers. That’s when experience stops being an asset and starts being the thing that blinds you.

Smart, experienced people keep making the same avoidable mistakes. In your view, is that a problem with decision-making structures or with the people themselves?

Honestly, both. What I’ve come to believe, after sitting on enough boards and watching enough smart people walk into the same walls, is that the structure shapes the person in the room. I’ve seen the same individual be genuinely courageous in one board setting and completely weak or passive in another. That tells you something important: it’s not about character, it’s about what the room permits. And most rooms are quietly designed often through habit and hierarchy, to permit agreement and discourage challenge. People adapt to that. They’re not weak; they’re human. The mistake is to build a room that rewards silence or strong passive consensus and then be surprised when you get it.

How does time pressure change the quality of boardroom decisions, and what should boards do differently when they genuinely cannot slow down?

Time pressure is where every good intention about process goes out the window. I’ve been in boards where a decision needed to be made in forty-eight hours and watched a board of genuinely capable people essentially hand the decision to whoever spoke first and sounded most confident. Not because they were lazy, because urgency creates a psychological permission to stop deliberating. It feels responsible to be decisive. It feels like leadership. What it actually is, most of the time, is the most senior, or loudest, voice in the room getting unchallenged airtime while everyone else tells themselves there’s no time for questions about a plan B.

The quality of the decision drops, and nobody notices because the speed feels like competence. What I’ve seen work is agreeing in advance on a one-sentence key question that has to be answered before any urgent decision is taken. Not a full process. Just one honest question that forces the room to pause for sixty seconds. That habit helps slow down and make better calls under pressure.

What actually happens after capital is deployed, and why do most investors pay too little attention to that phase?

What happens after deployment is that reality arrives. And reality is almost never what the investment memo described. I’ve seen this so many times that it no longer surprises me, but it still frustrates me. Investors spend months on due diligence, they negotiate every clause of the term sheet, they model the returns six different ways. And then the money goes in, and attention moves to the next deal.

The founders, who were impressive in a pitch room, are now running something three times more complex than they’ve run before, often without the right board, often without anyone to call when things get strange. And things always get strange. The first six to eighteen months after deployment are where companies either build the operating foundation that will carry them or develop the bad habits and governance gaps that will cost everyone dearly later.

I started paying much more attention to that phase after watching a perfectly structured deal deteriorate inside two years not because the thesis was wrong, but because nobody was minding the governance while the team was busy executing. The return on time invested in that post-deployment window is, in my experience, higher than almost anything you do at entry.

Family offices come with particular dynamics: family politics, generational differences, emotional attachment to assets. What governance principles matter most in that context?

Governance is fundamentally an emotional problem dressed up as a structural one. The frameworks matters but if you don’t understand what’s actually driving the people around the table, the frameworks are just paper. I’ve seen situations where a perfectly rational divestment decision became impossible because the asset in question was the company the grandfather built. I’ve watched generational transitions collapse not because the next generation was incompetent, but because nobody had ever had an honest conversation about who was actually in charge.

The principle I come back to most is separation or transition. Being clear about when you’re speaking as a family member and when you’re speaking as a shareholder or a fiduciary. Those are different roles and they pull in different directions, and pretending otherwise doesn’t make the tension disappear, it just makes it harder to name. The families I’ve seen navigate this well are the ones who had those uncomfortable conversations early, usually with an independent voice in the room who had no stake in keeping everyone happy. That role is the most valuable thing you can bring into a family office governance structure.

You have built private investment portfolios from scratch. What would you do differently today compared to early in your career?

Honestly, I would spend far less time on the financial engineering and far more time on the people and the governance, and I would do it before the money moved, not after. Early in my career I believed, like most people trained the way I was trained, that a well-structured deal protected you. Good terms, sensible valuation, clean documentation. And those things matter indeed. But I’ve watched well-structured deals go badly wrong because the board was an afterthought, and I’ve watched messy deals survive because the right people were around the table and could work through problems together. The structure doesn’t save you when things get difficult. The people do.

The other thing I would differently is being more honest, earlier, about situations that aren’t working. There’s a particular trap in private markets where you’ve invested significant time and capital into something, and the signs are there that it’s not going where it should, but you keep finding reasons to believe the next quarter will be different. I fell into that trap more than once. The sunk cost mixed with time pressure pulls at you in ways that are hard to describe until you’ve felt it. What I know now is that the moment you find yourself constructing reasons to stay rather than genuinely evaluating whether you should, you’ve already answered your own question. Getting out of your own way in those moments is harder than any financial model, and eventually more valuable.

What structural shifts in private markets are you watching right now that investors are not taking seriously enough?

The first element would be the speed at which deals are closing now. Competitive pressure has compressed due diligence to the point where you’re sometimes making commitments on information that would have been considered preliminary five years ago. Everyone knows this. Nobody wants to be the one who slows down and loses the deal. So, the market has collectively agreed to pretend that faster is fine, that experience compensates for thinner information and no plan B is really thought of. What it does is push all the governance work to after closing, when you have less leverage, less time, and a founder who just got the money and doesn’t particularly want a board telling them what to do.

The second thing is what’s happening in impact investing. There’s genuine capital flowing toward genuine problems, and I find that encouraging. But the governance infrastructure hasn’t kept up. You have idealistic founders, investors who are motivated by values rather than returns discipline, and boards that sometimes feel more like support groups than decision-making bodies. Nobody wants to be the one who introduces rigour because it feels like it’s against the spirit of the company or founders. That instinct is understandable and dangerous in equal measure. I’ve seen impact companies make decisions that a conventional portfolio company would never have survived, precisely because the shared values created a blind spot where critical thinking should have been.

You deliberately seek out mandates where something is broken or not working. What draws you to complexity and difficulty, and where do you find the energy to go into those situations?

I’m not sure I chose it as much as I discovered it was where I actually functioned best. Early in my career I had perfectly comfortable work objectives in well-run organisations with clear briefs and relatively predictable environments. And I did the work well enough. But I noticed I was more alive in more challenging situations. The ones where the diagnosis wasn’t obvious, where the people were under pressure, where the stakes were real. There’s something that happens in those situations that doesn’t happen anywhere else, people stop performing and start being honest or at least show their real personality. A board in genuine difficulty will have a more real conversation in one meeting than a comfortable board will have in a year. I find that clarifying rather than draining.

The other part of it is simpler: I genuinely believe that most broken situations are fixable, and that the thing standing between where they are and where they could be is usually not capital or strategy, it’s clarity. Clarity about what’s actually happening, what needs to change, and who needs to do what. Bringing that is something I know how to do and knowing you can help is its own kind of energy.

What I’ve learned to watch for is the situations that look fixable but aren’t, where the dysfunction is so embedded in the ownership or the personalities that no amount of clarity will shift it. I’ve walked into a few of those. You learn to read the weak signals earlier.

If you could give one piece of advice to a young finance professional, something that would have saved you real time and real mistakes, what would it be?

Learn to be comfortable with silence in a room. That sounds almost trivial, but I mean it seriously. Most of the mistakes I made early in my career happened because someone felt compelled to fill a silence with a position before they’d actually finished thinking. In finance, there’s enormous pressure to have a view, to project conviction, to be the person with the answer. That pressure is relentless and it starts very early. What it produces, if you’re not careful, is a habit of committing to positions before you’ve genuinely interrogated them, because the alternative, sitting with uncertainty, feels professionally dangerous.

The people I’ve respected most over thirty years are the ones who could say “I don’t know yet” without embarrassment, who could hold a question open longer than was comfortable, and who changed their mind without treating it as a defeat. That capacity, to stay genuinely uncertain is rarer than any technical skill I can think of, and more valuable in the long run than almost anything you’ll learn in a training programme or a business school. Nobody teaches it because it doesn’t look like a skill. It looks like hesitation. It isn’t.

The questions were asked by Binci Heeb.

Fulvio Maccarone, CFA, is a private markets investor and board member with over 30 years of experience at the intersection of capital, operations and governance. He has led the deployment of more than USD 2.5 billion into private investments across funds, co-investments, venture and growth capital, including senior roles at Pictet, BlueCrest and one of Europe’s largest family offices.

Today he invests in European deep tech and sustainability-driven companies — among them Woodoo, Enshift and Daphne Technology — and serves as a Non-Executive Director across companies in technology, healthcare, real estate and consumer sectors. He also teaches board governance to future directors at the Startup Board Academy and is completing doctoral research on cognitive biases in board-level decision-making.

Based in Geneva and working across five European languages, he advises financial institutions, family offices and boards on investment strategy, governance and complex restructuring.

See also: Fraud Isn’t Just Bad Luck—It’s the Consequence

The platform is designed to ease the workload on insurance advisors, automatically analyze policies, and, for the first time, integrate artificial intelligence into the day-to-day work of brokers in a truly practical way. In this interview, the two founders discuss their vision, the pitfalls of AI, the fight against inefficient systems, and why the future of the industry lies not in competition but in collaboration.

Silvio Siegenthaler knows the insurance industry inside out. Starting as a traditional insurance advisor, he worked his way up at Vaudoise to become one of Switzerland’s most successful client advisors. At the same time, however, the 31-year-old became increasingly fascinated by the world of artificial intelligence. Together with Michel Di Vito of Olai Interactive, he developed the Sothura platform, which is designed to automate brokerage processes while simultaneously improving the quality of consultations.

Swiss-based hosting, white-label compatible, and capable of integrating with virtually any existing software—all while adhering to strict data protection regulations for highly sensitive customer data. Sothura isn’t here to manage; it’s here to make insurance consulting smarter. Shortly before the official launch, the founders discuss their motivation, their technical breakthroughs, and their ambitions for the Swiss insurance industry. Is this the product the brokerage industry has been waiting for?

Silvio Siegenthaler, you had a successful career in the insurance industry for many years. What was the moment when you realized you wanted to start your own business?

Silvio Siegenthaler: Just imagine : a secure job, a good income, a career that many envied—and I threw it all away. With no safety net. Today that sounds brave; back then, it was a leap into the unknown, against all reason. But something inside me knew: If I don’t take the plunge now, I never will. I started out as a freelance broker and, on the side, I built a tool that turned my work upside down. At first, just for myself. Then I showed it to some consultant friends, and their reaction was electrifying. At that moment, I did something that’s actually forbidden for a salesperson: I stopped selling. I halted new business development, radically downsized my client base, and put all my eggs in one basket with this platform. I had to break free from the old structures to build my own. Because that’s exactly what our industry is missing: people with real-world experience who decide to take technology into their own hands instead of waiting for someone from the outside to do it. That’s when an idea became a mission.

You taught yourself how to program. How difficult was that while you were working in the insurance industry?

Silvio Siegenthaler: The real challenge isn’t the technique, but consistency. It’s the perseverance you need to focus on it without distractions: making consistent use of your time and repeatedly stepping outside your comfort zone. That was the hard part. Ich hatte mich schon rund zwei Jahre mit künstlicher Intelligenz beschäftigt – zuerst aus Neugier, dann immer ernsthafter – bis mich ein Bekannter endgültig in diese Welt hineinführte, die mich seither nicht mehr losgelassen hat.

I succeeded because I never saw this path as a sacrifice, but rather as a driving force. Today, as a freelancer, I develop and program practically nonstop. I see this consistency as a real advantage: most AI solutions for our industry are built by teams that have never sold a policy. With me, it’s the other way around, because I know the day-to-day of consulting from thousands of conversations and have brought the technology into my own work. This combination of domain expertise and technical depth is rare, and it’s precisely what’s embedded in every detail of Sothura.

Many people talk about artificial intelligence, but few actually build concrete solutions. What convinced you that the insurance consulting industry is ready for this transformation?

Silvio Siegenthaler: Because psychological distress and technology are coming together for the first time. Insurance advisors—whether they’re brokers, field representatives, or agents—have been struggling for years with tools that take more time than they save. At the same time, AI has reached a point where it truly understands language, contracts, and context—reliably enough for professional use. The question is no longer whether AI will enter the insurance consulting sector. The question is who will get it right first—in a Swiss, precise, and practical way, rather than as a gimmick. This is exactly the window of opportunity we’re tapping into.

Michel Di Vito, what motivated you to join Sothura as CEO of Olai and help develop the project from a technical standpoint?

Michel Di Vito: When Silvio showed me his prototype, I was honestly impressed. Over the course of my career, I’ve seen countless systems, many of them technically impressive. But rarely one that was developed with such foresight and attention to detail.

I was just as impressed by the person behind it all. Silvio had given up a secure, well-paying job and staked everything on his vision. Anyone who takes such a bold step and works with such consistency and integrity is clearly serious about it.

At the same time, it was immediately clear to me where the real challenges would lie: security, infrastructure, scalability, and the platform’s technological foundation. This includes real vector databases containing thousands of documents, an intelligent network of relationships between policies, lines of business, and insurers, as well as AI responses that are always based on real insurance documents. Added to this are data protection, FINMA requirements, and the pseudonymization of particularly sensitive data. Especially in this environment, regulatory compliance determines whether a platform can be operated at all.

That is exactly where my strengths lie. For over 21 years, I have been developing digital solutions at Olai Interactive for leading Swiss companies, including Coop Pension Fund, Bell Switzerland, and many others.

Sothura is one of the few projects I believe in so strongly that I’m investing in it myself. For me, everything about this project is right: the people, the technology, and the economic potential. My collaboration with Silvio has long since evolved into a friendship. And the progress we’re making shows that we’re on the right track: interest and inquiries from institutions are steadily increasing.

How do you position Sothura in the market, and where do you see the greatest potential?

Silvio Siegenthaler: SOTHURA SAFE understands data rather than merely managing it, and it is precisely this intelligence that many systems lack today. For brokers, this means, in concrete terms: less administration, more time for consulting, and better decisions. What truly sets us apart is our understanding of the consulting process: SOTHURA SAFE doesn’t just read policies; it understands the consulting process behind them. Sie erkennt, ob eine Deckung zur Situation des Kunden passt, wo eine Lücke klafft, was als Nächstes zu tun ist.

Most AI tools stop at data extraction or the inbox; we take it a step further, all the way to the actual consultation. On top of that, we have a clear home-field advantage: we’ve built the platform specifically for the Swiss market, using Swiss hosting and ensuring full compliance with local data protection and regulatory requirements. Very few others offer that. And that is precisely where we see the greatest potential: a platform that noticeably simplifies day-to-day work, enhances the quality of consulting, and thus becomes an integral part of the industry over time.

A major problem with generative AI is hallucinations. You claim to have solved this problem. How does that work, exactly?

Michel Di Vito: Preventing hallucinations is one of the most demanding challenges in AI systems. That is precisely why this issue has been a central component of our architecture from the very beginning.

Generic AI models tend to fill in missing information using probabilities. While this may be acceptable in many areas of application, it is not in the insurance sector. That is why our system is based exclusively on a controlled and verified knowledge base. Every answer is traced back to verifiable sources. If the necessary information is missing, no answer is generated; instead, the uncertainty is transparently disclosed.

The reliability of the answers is therefore not the result of subsequent corrections, but a direct consequence of the technical architecture. This is crucial for brokers: they must be able to trust that answers are based on reliable information. An AI that hallucinates when it comes to insurance law or regulatory issues is not an innovation, because it poses a significant risk.

Sothura relies on verified data and publicly available legal and industry standards. How important is data quality for reliable AI systems?

Michel Di Vito: Data quality determines whether or not you can trust an AI system. An AI system is only as good as the knowledge it is built upon. If it learns from unreliable or questionable sources, it will produce errors that sound deceptively convincing. No one in this industry can afford that. That’s why we’ve built our own specialized solution: based on a carefully reviewed, verified knowledge base of publicly available legal and industry standards, hosted in Switzerland and fully under our control. Every document undergoes classification and approval by us before the AI is allowed to work with it. This clean foundation is invisible and unspectacular, and that is precisely why it is our strongest advantage.

Brokers have been complaining for years about inconsistent policy presentations and inefficient processes. How exactly does your platform simplify their day-to-day work?

Silvio Siegenthaler: Today , every policy looks different because each insurer presents coverage, terms, and exclusions in a different way, and brokers have to painstakingly compare them by hand. That’s exactly what SOTHURA SAFE does for them: The platform scans the documents, standardizes their structure, and makes them comparable across all insurers. Was heute einen halben Nachmittag kostet, dauert Minuten.

But we take the entire day-to-day advisory process into account: The platform automatically identifies new potential clients from public registries, tracks tenders ranging from retirement planning to health insurance and cyber coverage, manages appointments, tasks, and meeting minutes, and keeps advisors up to date on every relevant change in the law or the industry. Particularly important: We also automate advisory documentation so that the legal advisory obligation under the revised Insurance Supervision Act (VAG) is reliably and future-proofed. In the end, the real benefit is not just the time saved, but the quality: The broker identifies coverage gaps that can easily be overlooked in day-to-day work. They become faster and, at the same time, better.

You mention that policies will be able to be automatically recognized, interpreted, and evaluated in the future. How far along are you technologically at this point?

Silvio Siegenthaler: This is no longer just a vision—it’s already a reality. Our AI reliably and accurately extracts data from policies—and not just policies, but also pension statements. A complete analysis takes less than a minute. The result is structured data that can be automatically imported into existing systems or a CRM. But we don’t stop at mere extraction: The system interprets the policy and compares it with the customer’s actual needs. We rely on recognized industry and classification standards and even take into account legally binding requirements from the respective work environment. Anyone familiar with the industry knows just how deep this goes. And the key point: Our pipeline updates itself automatically, year after year with new, up-to-date data, without anyone having to manually update it. It is precisely this step—from the lab to the real-world brokerage environment—that is more important to us than any show demo.

What role do APIs and the integration of existing CRM systems play in your strategy?

Silvio Siegenthaler: A key one . We integrate seamlessly into the broker’s existing workflow, and that is precisely one of our greatest strengths: thanks to our new technology and API capabilities, we can connect to virtually any existing system—including CRMs, industry platforms, and marketplaces. We’re a startup, not a company that’s grown over decades. Our technology stack is brand-new and extremely agile; we integrate and evolve faster than most. That’s exactly what makes us the natural partner for established providers: They bring their reach and established structures; we bring the specialized intelligence that generic AI simply cannot provide. Open interfaces are not an add-on for us, but a foundation. The future of the industry lies in connectivity, not isolation, and we are already in discussions with partners who share this vision.

How flexible is your platform in terms of language and compatibility with different providers?

Silvio Siegenthaler: We’re very flexible—and that’s by design. For one thing, we don’t just cover the four national languages. In the Swiss consulting industry, other languages are used on a daily basis, and that’s exactly what we offer, because the market is desperately seeking them and hardly anyone is providing them. On the other hand, SOTHURA SAFE has been white-label-ready from the start: A company can use the platform under its own brand and with its own branding as its own system, not as a third-party tool with our logo. This adaptability is not a side effect, but part of our strategy: We want to integrate, not impose ourselves.

In addition to the brokerage solution, you are also developing an AI-powered learning tool. Why is continuing education so crucial in the age of AI?

Michel Di Vito: Two years ago , I began developing an AI-powered learning platform for personalized memory techniques. Born out of a desire to give children with learning disabilities the same opportunities, LearnCards is one of the projects closest to my heart.

We will be implementing this project together as well. The SaaS solution will be available in Europe and the U.S. by the end of the year.

Together, we will integrate LearnCards into Sothura. The synergy is clear: Sothura provides proven industry expertise, while LearnCards ensures that this knowledge is retained long-term. This is an ideal combination, especially in an industry where ongoing professional development is mandatory.

Silvio Siegenthaler: Continuing education is not optional in our industry, as every insurance broker must obtain certification to keep their knowledge up to date. This is exactly where we’re taking it a step further: Since we already have specialized AI, it’s not a big leap to turn it into an adaptive application—one that not only imparts knowledge but also anchors it directly in long-term memory through a targeted methodology based on neuroscientific principles. Learning that truly sticks, rather than being crammed right before an exam and then forgotten afterward. But I’ll be completely honest: This is a standalone, second product that we’ll launch at a later stage. Today, our entire focus is on the broker solution, but the direction has been set.

Mr. Siegenthaler, you have invested about 3,000 hours in the project, and Mr. Di Vito has also put in well over 1,500 hours. What motivates you personally?

Silvio Siegenthaler: What drives me is the desire to solve a problem I’ve encountered firsthand in the field—for an industry I feel a strong connection to. I deliberately put my successful client base on hold and put almost all my eggs in one basket because I see potential that cannot be ignored: giving brokers and their clients a platform that finally creates space for genuine advice again. And I didn’t invest all those hours to make myself indispensable, but to build a system that works without me—one that thousands of brokers use every day, even when I’m not in the room.

Michel Di Vito: What drives me most is the work itself. I began specializing in AI over five years ago, long before it became all the rage. I develop my own neural networks from scratch. This goes far beyond simply using existing AI systems.

The insurance industry presents exactly the kind of challenges that require this level of depth: highly sensitive data, complex interrelationships, and absolutely no room for error. Solving problems like these is what inspires me. It’s the kind of challenge I’m passionate about.

I’m fully committed to this, and I say this with conviction: I’m the right person for the job. When you truly love what you do, you don’t keep track of the hours.

Many startups fail not so much because of their technology as because of their inability to penetrate the market. How do you plan to build trust with brokers, insurers, and partners?

Silvio Siegenthaler: You can’t buy trust —you have to earn it. Especially in this industry. Three things are crucial. First, background: I come from a hands-on background myself; I talk to brokers as an equal, not as a tech salesperson. Second, substance: Swiss hosting, verified data, data protection under Swiss law—no compromises when it comes to sensitive customer data. And third, attitude: We’re not here to take anything away from anyone. We collaborate with Union Swiss Brokers Holding AG and the established platforms and groups in the industry. Those who think in terms of networks build trust faster than those who try to do everything on their own.

When we speak again in two or three years: What would serve as proof to you that Sothura has truly transformed the industry?

Silvio Siegenthaler: The proof would be if a broker no longer viewed Sothura as an AI tool, but as a natural part of their work—just as no one is surprised by email anymore. When people in the industry no longer ask whether they’re working with AI, but rather with what. And when the standard for good advice has risen because the machine handles the tedious work, leaving people time again for what only they can do: listen, assess, and advise. When we’ve achieved that, we won’t have replaced the industry—we’ll have made it better. That, to me, would be the proof. Because for us, the future of insurance consulting begins right now.

The questions were asked by Binci Heeb.

Silvio Siegenthaler ( born in 1994) is an insurance broker, entrepreneur, and founder of SOTHURA—a results-oriented visionary. As a top salesperson, he worked his way up to become one of Switzerland’s most successful consultants before taking the leap into self-employment and developing SOTHURA SAFE. He is married, the father of two children, and lives in Lohn-Ammannsegg.

Michel Di Vito is the CEO of Olai Interactive and a technical co-developer of SOTHURA SAFE. With over two decades of experience in the digital industry, he is regarded as a technical pioneer. He is married, a family man, and an avid golfer, and lives in Oberdorf.

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With inncivio, Anna Raafat is developing a technology that helps users navigate complex digital environments from banking to insurance through intelligent real-time assistance. In this interview, she explains why financial literacy and AI-powered contextual personalization belong together and why many users feel overwhelmed by digital platforms .

As financial and insurance services become increasingly digital, many consumers struggle with jargon, product complexity and information overload. This is exactly where inncivio comes in: The US-based start-up, founded by Swiss and Portuguese co-founders, analyzes anonymized user behavior and provides contextual support at the exact moment users need it. The goal is not only higher conversion rates, but also more informed decisions through personalized and financial advice.

With a background ranging from the Swiss National Bank to Deloitte and the fintech sector, Anna Raafat combines expertise in business, technology and strategy. In an interview with thebrokernews, she shares her views on financial literacy, AI-powered customer engagement, trust in digital platforms and the future potential of the insurance industry.

Anna, you started your career at the Swiss National Bank before switching to consulting and the fintech sector. What ultimately motivated you to become an entrepreneur?

There were several reasons for this. Firstly, my family is very entrepreneurial; both my parents built a successful business and inspired me to build something of my own. Secondly, I am incredibly passionate about my work and want to make a positive difference. There is simply no better way for me to do this than by working with wonderful business partners to build something of my own for a cause I truly believe in.

Was there a particular moment when you realized that there was a real gap in the market that needed to be filled?

Yes, there actually was one. When I started working for a lending marketplace in Zurich, I realized that there is a growing gap between easy access to innovative fintech solutions and people’s knowledge of how to best use and benefit from such products (loans are just one example, but this extends to trading, cryptocurrencies, insurance and more!). Great products and great customer support are not enough for everyone to use them properly and maximize their returns, nor are they enough for these companies to maximize their returns. This sub-optimal balance can now be resolved thanks to advances in AI and the rapidly falling cost of computing power.

You mentioned that financial literacy worldwide is around 35 percent. What risks does this pose for society and the economy?

I firmly believe that knowledge solves most problems. Without the right understanding – for whatever reason – people make the wrong decisions (or make none at all), which has long-term implications on a macroeconomic level. When you combine this lack of knowledge with rapid technological advancement and near-universal access to everything, this becomes a critical problem. While modern users expect to make decisions quickly and effortlessly, access to information needs to be simple and frictionless to be truly effective. Companies need to take this into account to ensure that users make the most of digital products in a sustainable way.

How does your technology recognize when users need help navigating a digital platform?

Our technology anonymously analyzes user behavior in the specific context of a digital platform and accurately predicts when a user needs precise information and not just noise. Our predictive AI models determine what knowledge or information the user is missing in that specific context, at that location, in that format and at that time. This insight is then delivered directly to the platform via contextual cues, tooltips and pop-ups, ensuring that the user not only does more, but more importantly, does it better. Ultimately, we turn user confusion into trust and trust into transactions.

Behavioral analytics can raise privacy concerns. How do you deal with trust, anonymization and data protection?

Absolutely. We have extensive experience in this area, having been active in the fintech sector worldwide for many years. We follow strict measures to ensure data protection:

1. We do not track personal data, but instead focus on anonymous behavioral “footprints”
2. In addition, all data is encrypted using multi-layered approaches
3. We are also SOC2-compliant, ensuring best practices in data and information management within the company

You emphasize that the goal is not simply more transactions, but better decisions. How do you define a “better decision” in digital finance?

Of course, this depends on the focus of the platform, whether it is a trading platform, a credit marketplace or a neobank, the goals will be different. By “better” I mean “well informed” from the user’s point of view. Sometimes you see quite intrusive pop-ups that are designed to get users to make more purchases at any cost. This may increase a platform’s sales in the short term, but if users don’t properly understand the actions they are taking, they will ultimately lose trust. I believe that when you truly understand a financial action, you have more control over it; you achieve better results and continue to transact in the long term. This creates a win-win situation for both the end user and the company, which usually relies on a transaction-based revenue model.

Why do you think the insurance sector is particularly well suited to your technology?

Beyond the financial sector, I am convinced that the insurance industry is another sector that needs our technology. Ultimately, any transaction-based platform with a certain level of complexity needs this kind of advisory layer. An insurtech absolutely fits into this category.

Insurance products are often perceived as difficult to understand. Where do you see the biggest communication gaps between insurers and customers?

For retail customers to make an informed decision about which insurance product to take out, multiple layers of information need to be clear. While digital platforms do a great job of simplifying the user experience, it is still a “one size fits all” approach; not everyone has the same information needs to make a decision. Thanks to today’s technological advances, it is possible to identify what each individual user is missing in a particular case and fill that gap efficiently, in real time and in the right place: be it by explaining complex technical terms, explaining the scope of insurance or clarifying what information is currently missing.

What distinguishes inncivio from conventional chatbots, FAQs or static information services?

Above all, inncivio is contextual and proactive. We are convinced that it is already too late to wait for a user to ask a question – simply because sometimes you don’t even know how to formulate that question. We believe that the future lies in predicting what you are missing in real time and providing it accordingly. Furthermore, personalization requires the right place, the right time and the right format. FAQs and static information offerings are wonderful sources of information, but they are the same for everyone. But user A may be more visually oriented than user B, while user C may be more experienced than user D. It doesn’t make sense for everyone to see exactly the same content. Today’s technology can solve that. Also, while education is great, what users often lack is the connection between information and action. Without this bridge, content becomes too theoretical or abstract. We want to close this gap.

Do you think that digital platforms will eventually anticipate users’ knowledge gaps before they even realize that they need help?

Yes, definitely!

You grew up in France, are German and now also Swiss and have a multicultural background. How has this shaped your view of innovation and business?

There is so much beauty and diversity on this planet; taking the best from every culture you encounter is what I love to do, both personally and professionally. That’s also why at inncivio, although the team is still small, we already cover several continents, languages and cultures. Nevertheless, we have a common mission that we are all working towards together!

What makes Zurich and Switzerland attractive locations for fintech and AI start-ups?

I actually see similar trends to those in New York: a vibrant multiculturalism that is incredibly noticeable on a per capita basis. It is precisely this diversity that drives strong innovation and progress. Switzerland also has excellent universities, which makes it a sustainable R&D center on the global stage.

Where do you see inncivio in five years’ time, and what role will AI play in the everyday lives of banking and insurance customers by then?

I see inncivio as a contextual orientation layer in the entire transactional digital economy, reaching far beyond the finance and insurance sector. It acts as a bridge that enables platforms to empower their customers while generating solid, recurring revenue. We would essentially become the “invisible hand” or the “ultimate revenue infrastructure” for the transactional economy.

The questions were asked by Binci Heeb.

Anna is the Co-founder and COO of inncivio, an agentic revenue layer for fintech and financial services. An economist by education, she previously worked at the Swiss National Bank and Deloitte, specializing in macroeconomics and strategy consulting for the banking sector. After identifying critical knowledge gaps in the fintech industry, she co-founded inncivio to solve them. Fluent in German and French, she’s also proudly defending a nearly 800-day Spanish streak on Duolingo!

Read also: Swiss InsurTechs are gearing up for London – and looking for capital, data power and sales experts

When every second counts in a cardiac arrest or avalanche accident, technology can make the difference between life and death. This is precisely where the Swiss company SureVIVE comes in. With its “Momentum” platform, CEO Georg Hauzenberger is developing digital solutions to bring rescue teams together more quickly, in a more coordinated and efficient manner.

In the podcast, the electrical engineer and former Swisscom manager talks to Jakob Barandun (Capricorn Connect) about the vision behind the company, the challenges of modern rescue systems and why artificial intelligence will play an even more important role in the future.

Alerting in seconds instead of minutes

SureVIVE develops mobile alerting and situation management systems for first responders, rescue services and specialized emergency services. The “Momentum” platform is now used in 22 out of 26 Swiss cantons and supports over 40,000 emergency services.

Every minute counts, especially in the event of cardiac arrest. Whereas in the past, emergency services were sometimes organized via lengthy telephone conferences or pagers, today hundreds of volunteer first responders can be alerted simultaneously within around 15 seconds. The app automatically selects suitable first responders according to location or available equipment such as defibrillators.

However, Hauzenberger emphasizes that technology is only one part of the overall system. Strong first-responder networks, sufficient defibrillators and cooperation with communities and organizations are also crucial.

A common picture of the situation for all rescuers

In addition to raising the alarm, the main focus is on networking various rescue organizations. With “Momentum Pro”, emergency services on site receive a joint digital situation picture in real time. This means that mountain rescuers, paramedics, air rescue or slope rescue services can simultaneously see who is already on site, when a helicopter will arrive or which resources are still needed.

The benefits of such systems are particularly evident in avalanche operations. Different organizations – from mountain rescuers and helicopter companies to avalanche dog handlers – have to be coordinated in the shortest possible time. Surevive aims to ensure that everyone involved receives the same information in real time.

Hauzenberger describes the overarching goal with the guiding principle “Tearing Down Boundaries to Save Lives”. This refers to the quick and easy exchange of data between different rescue organizations.

Swiss Emergency Data Hub as a digital hub

The “Swiss Emergency Data Hub” is a key project for the future. This is a joint data exchange platform for various rescue organizations and cantons.

The idea behind it: Mission data, status reports and position information can be shared across organizations in real time. Rescue services, mountain railroads, air rescue services and emergency call centers thus receive an identical picture of the situation and can coordinate their resources more efficiently.

For Hauzenberger, this is precisely the key to modern rescue systems: fewer technical and organizational barriers and more cooperation between all those involved.

Artificial intelligence to further optimize operations

Artificial intelligence is also playing an increasingly important role at SureVIVE. The company now has data from more than ten years of deployment history. In future, this data will be used to make alerting algorithms more intelligent.

The aim is to select emergency personnel even more precisely and dynamically adapt systems to changing situations. Hauzenberger attaches great importance to data protection and the responsible use of data.

AI is not intended to replace people, but to optimize processes and support emergency response organizations in reacting faster and more efficiently.

Meaningfulness as an employer brand

As an employer, SureVIVE places a strong emphasis on meaningfulness and personal responsibility. According to Hauzenberger, many employees make a conscious decision to work for the company because they want to make a concrete contribution to society through their work.

The corporate culture is pragmatic, team-oriented and strongly characterized by people who themselves have experience in the rescue sector. The common goal is clearly defined: To develop technologies that provide the best possible support for rescue workers in an emergency.

Binci Heeb

See and read also: When leadership makes you lonely

Experience is being weeded out, while the shortage of skilled workers is growing. Business psychologist Prof. Dr. Lioba Werth explains why companies are failing strategically when it comes to ageing and why real leadership today has less to do with tools than with attitude.

Demographic change, a shortage of skilled workers and increasing pressure to be efficient are posing fundamental questions for companies. At the same time, the understanding of leadership is also changing: away from methods and management tools and towards reflection, responsibility and an inner attitude.

Business psychologist and top-level consultant Prof. Dr. Lioba Werth combines both topics. She has been working with managers at board level for decades and observes how power, age and values often unconsciously shapeactions. Older employees often play an underestimated role in this. In an interview with thebrokernews , she talks about the “courage of ageing”, the underestimated risks for companies and why experience could become one of the decisive competitive factors in the future.

Professor Werth, you have played an active role in shaping the development of business psychology in Germany. What originally fascinated you about the combination of psychology and business and what fascinates you about it today?

I was irritated early on by how irrational economic decisions often are – and how rationally they are justified at the same time. Psychology shows: People rarely make decisions objectively. They make decisions – and then build a convincing story around them.

Today, it is precisely this blind spot that fascinates me. Because that’s where the most expensive mistakes are made – and the most exciting aha moments.

Your career path has taken you from caring for the dying to science and top-level consulting. What experiences have shaped your current understanding of leadership the most?

End-of-life care was my most radical school. There it very quickly becomes clear what works in life – and what doesn’t. At the end, no one talks about key figures or great successes. Many people talk about what really moved them – and that they regret not having dared to be clearer. This clarity is also often lacking in management. And that’s exactly where I come in today.

Science has taught me to look closely: To recognize connections, to understand levers and not to follow the obvious too quickly.

And my work with managers and my own leadership role have confirmed one thing to me time and again: It’s all about people – even at the highest level. And sustainable impact is never achieved through strategies or tools alone, but through attitude and a clear code of values.

These three perspectives still shape my understanding of leadership today.

Today you work with board members and top executives. What makes the challenges at this level fundamentally different from classic management development?

The higher the level, the more subtle the problems. It’s less about ability – and more about self-perception. At the top level, the biggest risk is not incompetence, but overestimating oneself in combination with loneliness. It gets quiet and lonely at the top. Honest feedback becomes rare. And that is precisely what makes it dangerous. Top executives therefore rarely need more tools, but rather mirrors and targeted reflection (as well as a resilient relationship with the base).

Werth quote: “Tools provide security. Attitude gives direction. Many companies have too much of one – and too little of the other.”

You say that managers need less tools and more reflection. What does that mean in concrete terms in the everyday life of a CEO or board member?

Many managers are excellently trained – and surprisingly unfamiliar with themselves. A board member/GM is usually highly experienced, experienced and confident in their appearance – and they have to be. But it is precisely this role that characterizes them: making decisions under uncertainty, setting the direction, standing at the front. This trains you one-sidedly and changes you – insidiously.

Reflection therefore does not mean thinking longer, but looking more honestly. Am I making this decision out of conviction – or out of convenience? Because it is right – or because it feels good? It sounds simple, but it is highly effective. If you can’t observe yourself accurately, you quickly become a pawn of your own patterns – especially in positions of power.

Reflection is therefore not an optional extra, but risk management. And it rarely works alone. It requires a sparring partner at eye level – with power sensitivity. Because a coach who does not understand power in its context cannot effectively support leadership at this level.

Werth quote: “The biggest risk in leadership is not making the wrong decisions – it’s thinking you’re infallible.”

You confront managers with the question: “What does power do to you?”. What typical changes do you observe in people in positions of power?

Power changes perception. People in positions of power experience less contradiction, feel more secure and at the same time become more susceptible to making wrong decisions. The paradox: we get into positions of power because of certain characteristics – and it is precisely these that change there. The dangerous thing is that those affected don’t realize it and don’t know how to counteract it effectively.

Werth quote: “Power corrupts – unless we counter it with something effective.”

Topics such as courage, responsibility and trust seem almost “old-fashioned”. Are you experiencing a renaissance of these virtues in modern corporate management?

These concepts were never gone – we just thought we could replace them with methods. Now it’s clear that in complex situations, no tool will ultimately help. Then personality is the deciding factor. And suddenly ‘old-fashioned’ virtues seem ultra-modern again.

How can organizations ensure that managers do not lose touch with their own values despite pressure, speed and responsibility?

Not at all – if they don’t actively shape it. Values are revealed under pressure. And this is precisely where they are often lost: not out of malicious intent, but due to speed, targets and social pressure. Three levers are decisive:

Firstly, binding reflection formats.
Not optional, but institutionalized – for example, regular sparring sessions or decision reviews.

Secondly: Systematized contradiction.
For example, through clearly defined roles in decision-making bodies whose task it is to bring in critical perspectives. Without a legitimized opposing view, the hierarchy always wins.

Thirdly, the measurability of behavior.
Is a manager also measured by how they have achieved results – or only by the fact that they have achieved them?

If these structures are missing, the shortest-term solution almost always wins.

Werth quote: “Values are not reflected in the mission statement – but in decisions made under pressure.”

You deal intensively with the topic of ageing, both personally and professionally. Why do you think this topic is still underestimated in companies?

Because we misunderstand age.

On the one hand, we confuse it with degradation. Yes, some abilities decrease (e.g. speed). At the same time, however, others are increasing: not only experience, but also judgment, tolerance of ambiguity and emotional regulation. These are all qualities that companies urgently need in their employees.

Secondly, we think in terms of costs (older employees cost more than younger ones) and not in terms of skills. As a result, we underestimate what this loss of expertise really means for the company.

Werth quote: “We underestimate what this loss of expertise really means for the company.”

From a psychological point of view, what changes do decision-makers and leaders in particular undergo as they get older?

With age, it is not so much the ability as the quality of decision-making that changes: less impulsive, more integrative. Less quick, more coherent and sustainable in the long term. More values often come into play. The problem: organizations reward speed – not quality.

You talk about “courage in getting older”. What exactly does that mean and why is this courage so crucial?

You don’t need courage to grow older (because we grow older automatically) – but you do need courage to grow older skillfully. The real challenge is to develop your own identity. It takes courage to let go of roles that are no longer equally valid. And it takes courage not to withdraw inwardly, but to continue to experience yourself as effective – even under changed conditions. This is precisely the crucial point: many people do not lose their competence as they grow older, but they do lose their confidence in their own relevance.

Courage when getting older therefore means not making yourself quieter, but repositioning yourself. Those who do so gain serenity, clarity and dignity.

Many companies, including in the insurance industry, are currently shedding employees aged 55+. Do you think this is a short-sighted strategy?

This makes economic sense in the short term, but is often strategically risky. If companies systematically reduce the number of employees aged 55+, they save costs in the short term – but at the same time lose a central component of organizational judgement. Experience is not a static body of knowledge, but a decision-making filter/aspect of quality under uncertainty. And it is precisely this quality aspect that quietly disappears until it is suddenly missing in critical situations. This is why demographics is not just an HR issue, but a component of risk management.

Werth quote: “Demographics is not an HR issue – it is an underestimated component of risk management. If you lose experience, you don’t lose the past, you lose decision-making quality in the future.”

What risks arise for companies when they deliberately rely on younger, cheaper workers and lose experience?

When organizations consistently rely on younger, less expensive employees, they gain speed – but often lose directional security. Younger employees are generally excellently trained, digitally competent and efficient. What they naturally lack is not competence, but condensed experience in dealing with complex, contradictory or crisis-ridden decision-making situations. This experience cannot be accelerated and cannot be replaced digitally. If it is lacking, the probability of wrong decisions increases – especially where exceptions rather than routine are involved.

The decisive factor is therefore not either/or, but a clever combination of both perspectives.

Werth quote: “We afford ourselves the luxury of sorting out experience –
and wonder about the shortage of skilled workers.”

Which models do you see as particularly useful for keeping older employees in the company and utilizing their knowledge?

In my opinion, the question is posed incorrectly: Not “How do we retain older people?”, but “Where does experience unfold its greatest leverage?” The answer is clear: in mentoring, in complex decisions and in critical situations. This is where its real value is revealed. Experience is not a residual item – it is a competitive advantage.

Another point that is often underestimated is personnel development. Many offers are implicitly geared towards younger employees. However, older employees have different topics and needs – both in terms of content and the way they learn. Learning is not worse, but it works differently. If you don’t take this into account, you lose attractiveness.

I find models that go beyond traditional employment exciting. One example is Sentaris GmbH: a platform that brings together experienced retired specialists with companies – specifically for project-related or short-term assignments. (Whitepaper here)

However, the real added value lies in the combination: transitions are not only organized, but actively shaped – both on a personal level, for example through coaching, and on an organizational level through targeted knowledge retention. It is precisely such approaches that show how experience can be used strategically instead of simply being phased out.

Werth quote: “Companies don’t lose experience because people leave – but because they don’t have a framework for using it sensibly.”

You work with insurance companies and banks. To what extent do these sectors underestimate the needs of an ageing society, both as employers and as providers of products?

The central underestimation does not lie in people’s age, but in the idea that life courses are still stable and can be clearly planned. People are not just getting older, they are living much differently today. And it is precisely this ‘different’ that has not yet been incorporated into many products and HR strategies. Banks and insurance companies often still think in terms of traditional life phases: Education, working life, retirement.

However, the reality is different: Life courses are less linear, transitions are more frequent and more individual. At the same time, the demands of old age are increasing – financially, in terms of health and biographically. Many insurers have therefore set out to reflect this variability of life in their products – but they have certainly not yet reached the end of the road.

If you had to make one key recommendation to boards of directors in the insurance industry today, what should they immediately think or do differently when dealing with demographics, leadership and risk?

The real risk lies not in the ageing society, but in an outdated logic of risk assessment. As long as risks are primarily defined by age segments, a systematic blind spot remains – and this is particularly expensive in a risk industry.

The consequence is therefore clear: away from static segments and towards progression-oriented risk policies. It is not age itself that is decisive, but the dynamics of life courses – in other words, the question of how risks change over time, transitions and breaks. Understanding this shifts the focus from product logic to the reality of life – and this is precisely where the competitive advantage will arise in the future.

What psychological ability will decide in the coming years whether management in banks and insurance companies will be more stabilized or risky?

In my opinion, the decisive psychological skill of the next few years will be the ability to anchor oneself under uncertainty. We are operating in a working and economic reality in which stability is decreasing, complexity is increasing and quick answers are becoming less and less viable. In such contexts, it is no longer primarily knowledge that separates us from ignorance, but rather inner stability from inner reactivity.

Managers will therefore be measured less by how much they know or how quickly they make decisions – and more by whether they can still regulate themselves well in complex situations. In concrete terms, this means recognizing their own patterns, not reacting automatically, enduring contradiction and still remaining capable of acting. After all, it is not the loudest or quickest who will lead effectively, but those who can remain calm under pressure. And this is not a question of technology or a toolset, but of psychological maturity.

The questions were asked by Binci Heeb.

Prof. Dr. Lioba Wert: Education: Degree in psychology (Dipl. Psych.) at the University of Trier, doctorate (Dr. rer. nat.) also at the University of Trier and habilitation at the University of Würzburg.

Professional experience: Active as a keynote speaker, coach and author. Professor of Business, Organizational and Social Psychology (W3) at the Catholic University of Eichstätt-Ingolstadt and Chair at the TU Chemnitz and the University of Hohenheim from 2004 to 2015. Also founder of the Zentrum für Training & Weiterbildung (ZTW e.K.) in 1999.

Industries: Science and medicine, banks and insurance companies, corporations and SMEs as well as chambers and associations.

Consulting focus: leadership, succession and the management of upheaval, self-regulation and the topic of ageing – both individually and in a corporate context.

Read also: Older, more expensive, indispensable?