An asset can be tokenized today. But what happens if the token is tradable, yet the market remains illiquid, or if the digital claim does not correspond to legal ownership? This is precisely where the real insurance question arises.

Let’s imagine a piece of real estate for a moment. Not just any piece of real estate, but a building in Zurich. So far, it’s pretty clear what that entails: ownership, the land registry, financing, rental income, and, of course, insurance. Now, the economic rights associated with this property are being represented digitally. An asset that is difficult to divide is transformed into digital units that can be transferred and potentially traded on a secondary market. Technically, much of this is already possible today. But as soon as we take it a step further, the story gets more interesting. What does the buyer actually own? The token? A share of the economic value? A legal claim? And what happens if the digital infrastructure works but the underlying market suddenly becomes illiquid? It gets even more interesting for the insurance industry.

What risk is an insurer actually supposed to cover in this case: the asset, the token, or the infrastructure in between? At first glance, these questions seem technical. In fact, they go straight to the heart of the insurance business: What constitutes value, when does that value give rise to a risk, and what is the basis for our confidence that a claim will ultimately be enforceable? This is precisely where the story of tokenization for the insurance industry begins.

From Digitization to Tokenization

The financial sector has been undergoing digital transformation for decades. First, information became digital; then, processes did. Today, we are witnessing something different. Economic rights and assets themselves are increasingly being represented digitally. That is the crucial difference. A digitized document is not yet a digital asset. A tokenized asset, on the other hand, can be structured in such a way that certain rights are digitally represented, transferred, and—depending on the legal and technical design—can even be partially programmable. This changes not only the technology behind a transaction but also how value is transferred within the financial system.

The Bank for International Settlements (BIS) now describes tokenization as a potentially transformative development in financial market infrastructure. In its vision of a “Unified Ledger,” tokenized central bank reserves, commercial bank money, and financial assets could interact with one another on a programmable infrastructure. And this discussion has since progressed further. In March 2026, the Eurosystem presented its Appia Roadmap. The goal is to develop a European tokenized financial ecosystem in which central bank money continues to serve as the monetary anchor. The initiative is set to be further developed by 2028 and is intended to bring together both public and private market participants.

Things became even more concrete with Project Agorá. In May 2026, the BIS reported that a prototype had demonstrated how tokenized central bank reserves and tokenized commercial bank deposits could support cross-border wholesale payments. Work is now set to continue toward transactions involving real-world assets (BIS, 2026). This is noteworthy. It means the discussion has moved somewhat away from the original crypto narrative.

Today, the question is no longer simply whether blockchain can make certain financial transactions faster or cheaper. The bigger question is: What happens to a financial system when money, assets, and contractual logic become programmable? For insurers, this is a pivotal transition. After all, insurance companies do not wait until new market structures are fully established before responding to change. They must assess risks while these structures are still emerging.

The most interesting question is not a technological one

In my dissertation, “From Code to Capital: A Study of How Emerging Technologies Shape Stock Markets” I have been examining the impact of emerging technologies on financial markets. One perspective that has emerged from this research seems particularly relevant to today’s discussion on tokenization: The economic impact of a technology cannot be derived solely from its technical capabilities. There is a crucial factor that lies between a technological possibility and its actual adoption: the expectations of market participants. Technological innovation therefore does not initially create efficiency alone. It also creates uncertainty. And uncertainty influences markets. That is precisely why, when it comes to tokenization, I am less interested in the question of whether a token can technically function. Far more intriguing is the question: When will market participants begin to align their behavior with this new infrastructure? For it is at that moment that technology becomes economic reality.

The Illusion of Automatic Liquidity

Let’s take our real estate example again. So far, it’s been difficult to sell a share of it. A token could significantly simplify this process from a technical standpoint. Perhaps in the future, an investor will be able to purchase a small share and transfer it at virtually any time. That sounds like liquidity. But is it really? It’s worth taking a closer look. Technically, a token can be transferable around the clock. That by no means implies that a buyer is available at all times. It requires a functioning market, price discovery, and trust, as well as a sufficient number of market participants.

It is precisely this distinction that has now become apparent in international research as well. The International Monetary Fund (IMF) concludes that tokenization can reduce certain inefficiencies in the life cycle of financial assets. At the same time, however, new inefficiencies and risks may arise—for example, due to greater interconnectedness, smaller liquidity buffers, higher debt, faulty code, or increased Concentration of Market Infrastructure. This means we need to put the attractive idea of tokenization into perspective: While tokenization can create tradability, it does not automatically create a market. For an insurer, this is not an academic distinction. Valuation and liquidity are central components of risk management. When an insurer holds a tokenized asset, it must not only know that the asset is technically transferable; it must also know the asset’s actual value in a stress scenario. And this is precisely where technology and traditional financial market risks converge.

What does the investor actually own?

The question becomes even more fundamental when we shift our focus from the market to the law. A token can represent a stock, a bond, a fund share, or a claim to a real asset. However, the token itself is not automatically equivalent to the economic or legal claim.

At first glance, this sounds trivial. But it isn’t. Because the more financial assets are represented digitally, the more important the connection between technical representation and legal reality becomes. A smart contract can automatically execute a transfer. But it cannot decide on its own whether the underlying claim is legally enforceable. A ledger can unambiguously document a transaction. However, it does not automatically answer the question of who is the owner in the event of a dispute. This creates an interesting tension: The technology can make a transaction unambiguous, while the legal significance of that transaction may still remain complex. This is central to the insurance industry. After all, insurance does not protect code; it protects an economic interest.

Risk doesn’t disappear. It just shifts.

Perhaps this is the most important insight for insurers. Tokenization will not simply make risks disappear. It will, in part, shift them. A traditional asset carries known risks. When it is tokenized, additional risks may arise: cyber risks, smart contract risks, data and oracle risks, custody issues, or dependencies on specific digital infrastructures. The IMF points out precisely this ambivalence: The greater integration and programmability of tokenized financial markets can enable efficiency gains, but at the same time can also create new interconnections and concentrations, as well as technical risks. In 2026, the BIS also emphasized that digital innovation creates new macroprudential challenges and brings the question of maintaining trust in money and financial infrastructure to the forefront. This changes the insurer’s role. The insurer must not only ask, “What could go wrong here?” but must increasingly ask, “On which infrastructure could something go wrong?”

That’s a small linguistic difference. But it could make a big difference for future underwriting.

And suddenly, the broker becomes interesting

This is precisely where I see a unique opportunity for brokers. In a tokenized financial world, brokers could take on a new intermediary role. On one side is the client with their assets and financial interests. On the other side are insurers, reinsurers, and potentially the capital markets. In between lies a new layer: digital infrastructure. The broker may then need to understand not only what is to be insured, but also how the underlying asset is structured from a technical and legal perspective. In the future, a client might no longer simply ask, “Is my asset insured?” but rather, “What happens to my insurance claim if the digital infrastructure fails?” Or: “What happens if the token is tradable but the underlying market collapses?” Or: “Who bears the risk of an error in the smart contract?” As a result, technology does not become an additional specialty alongside insurance. It becomes part of the risk description itself.

Trust Becomes the Crucial Issue

And that brings us to a point that, in my view, is even more important than blockchain or tokenization itself: trust. The insurance business is based on trust. The customer trusts that his claim will be honored in the event of a loss. The insurer relies on data. The reinsurer relies on the primary insurer’s risk models. The investor trusts in the functionality of the financial market infrastructure. Tokenization can technically support certain elements of this trust. It can make processes more transparent. It can synchronize information. It can automate certain workflows. But it does not replace trust. A token can function perfectly from a technical standpoint and still represent a claim that is economically or legally unclear.

That is why I would not view tokenization as a substitute for trust. Rather, it could become part of a new trust infrastructure. This is also interesting because the BIS itself explicitly links the future of the tokenized financial world to maintaining trust in money and institutional structures.

What comes next?

This puts us at an unusual juncture. The technology has advanced far enough that central banks, international institutions, and major financial market participants are exploring specific infrastructures and applications.

At the same time, it is by no means certain which models will be economically successful. It is precisely this combination of technological progress and lingering uncertainty that makes tokenization so interesting. Perhaps it will fundamentally transform the financial market infrastructure. Perhaps certain applications will prevail, while others will disappear. Perhaps the technology will ultimately prove less revolutionary than is currently expected. But even then, one important question remains: What do we learn about risk when the infrastructure on which value is created and transferred changes? For the insurance industry, this could be where the real opportunity lies. Not in tokenizing every asset as quickly as possible, but in understanding sooner than others which risks—and which new forms of value and trust—emerge from this process. The crucial question, therefore, is not: Who will tokenize first? But rather: Who will understand first how risk, value, and trust are transformed by this process?

Laura Arenas

Laura Arenas is an internationally experienced executive specializing in risk and digital resilience, as well as a researcher and author. She helps organizations navigate risks associated with digital and emerging technologies, strengthen resilience, and safely manage transformation. With over 10 years of international experience at leading financial and technology companies, she combines industry practice, research, and strategic decision-making. Laura earned her Ph.D. in business administration with honors (cum laude) and received the Extraordinary Doctorate Award for the best dissertation. In 2025, she was honored by Risky Women as a “Woman to Watch.” She is also a member of the Executive Committee of Global Women in AI and has served as a Global Ambassador for Women in AI since 2025.

See also: Beyond Profit: Building a People-Centered Economy in a World Obsessed with Money

The example of Generali Austria shows how insurers can use a new attribution approach to gain actionable real-time insights to optimize digital advertising campaigns and increase their online leads.

Digital marketing offers companies exciting opportunities today. They can use the enormous reach of a variety of channels: from websites and apps to social media. Modern technologies provide information about users’ behavior, location or life situation. On this basis, target groups can be addressed more individually than ever before via personalized ads. According to Bitkom, German companies spent 30.9 billion euros on digital advertising in 2024. With success: the Bitkom study “Digital Marketing in Germany” concludes that every euro invested generated at least 2.50 euros in revenue.

Optimize digital strategy

In its latest Digital Insurance Maturity Study 2025, Deloitte sees the insurance industry under pressure to optimize its digital strategies: “Customers expect their provider to offer a convenient digital customer journey across all steps – from searching for information, taking out a policy and paying, to reporting a claim and terminating the policy.” On average, however, local insurers do not meet these high expectations in any of the areas. One reason for this is that they often work with complex, often outdated IT systems. These systems are difficult to integrate with modern, digital solutions. However, a prerequisite for competitive digital marketing is to integrate AI, big data or real-time analyses into these systems.

Another obstacle is the customer journey. The decision-making process for insurance products is long. Users often have an above-average number of contact points until they take out a policy. And this happens both online and offline: on comparison portals, in social media and search engines, via printed brochures or in personal consultations. This is because traditional advertising methods and personal contact remain important in the insurance industry: it sells sensitive and consultation-intensive trust goods such as life or comprehensive health insurance. A survey of GDV member companies shows that in 2024, more than one in five insurance contracts (22%) were concluded digitally. However, just under 80 percent of contracts were concluded with personal support. What’s more, many users now switch devices several times during the process: from laptop to smartphone and back again. The view of the customer journey remains incomplete and budget decisions are made on the basis of inconsistent data and assumptions.

Insurers in a complicated field of tension

In today’s highly digitalized and dynamic advertising landscape, the industry finds itself in a complicated field of tension: on the one hand, there is increasing pressure to reach target groups with data-driven, personalized offers in digital channels. At the same time, personal contact remains important. An end-to-end digital customer journey, which would lead to a complete database, is therefore only possible to a limited extent.

In addition, the correct handling of tracking technologies and third-party data from external platforms is becoming increasingly complex for advertisers in Germany: access to user data and its use has been restricted by regulatory requirements and legislation such as the General Data Protection Regulation (GDPR). The development of alternatives, such as strategies for first-party data from proprietary systems, is complex, costly and labor-intensive. This leads to an incomplete data situation, which makes it difficult to manage digital campaigns efficiently.

According to the GDV, the main problems facing insurers in Germany are declining measurability, high regulatory pressure and the need to combine digital marketing with advice, trust and legacy systems. The challenge therefore lies not only in technological innovation, but also in combining it with a controlled, compliance-proof use of digital advertising channels.

Only campaigns based on correct data are effective

Which channels online and offline actually lead to sales? Where were the most contacts made with the target groups? And where should marketing therefore invest budget and human resources in future? If you don’t just want to record the last click or the last conversation before the signature, but are looking for reliable answers, there is no way around precise attribution. Advertising professionals have been trying to determine the effectiveness of their campaigns for decades. The aim of this measurement, known as attribution, is to use advertising budgets as efficiently as possible. Reliable information about existing and new customers is crucial for business success. In recent years, the use of cookies has been severely restricted by technical and regulatory requirements. As a result, performance marketing lacks important data for analysis and planning.

In addition, contradictions arise across platforms: different providers such as Google, Microsoft Ads, Meta or Tiktok deliver different figures with their own in-house measurement methods. Added to this is the complex customer journey described above. In this situation, traditional attribution models are of little help in making decisions based on reliable data analyses and allocating budgets in a targeted and efficient manner. According to the MMA Global 2024 survey, over 61% of marketing managers admit that they no longer trust their attribution metrics. Marketing teams are facing an attribution dilemma.

Generali Austria pioneered new attribution approach

The example of Generali Austria and Nexoya shows how a correct and complete database can be determined for the successful optimization of digital advertising campaigns. Nexoya, a platform for AI-based cross-channel attribution and optimization, has developed an integrated, regression-based attribution (RBA). The privacy-compliant feature overcomes the limitations of cookie- and pixel-based analytics and delivers actionable, real-time insights.

Every week, each user receives updated budget and target recommendations for all campaigns and across all marketing channels, which can be implemented at the click of a mouse. By analyzing historical time series data, which is regularly updated, the system can statistically reconstruct the actual impact of each channel. The continuously updated performance data from the channels serve as “mini-experiments” for attribution in order to improve the regression.

This is a completely new approach: for the first time, Nexoya combines statistical attribution with weekly experiments fed with data from over 40 integrable platforms. This interaction changes the way budgets are managed: Marketing teams don’t just get numbers, they get an AI-based assistant that learns continuously, implements immediately and allocates budgets where they really make an impact.

The potential of the new attribution has already been demonstrated in practice: Generali Austria was one of the first customers interested in Nexoya’s new attribution approach. The company is part of the Generali Group, a leading global insurance group. The marketing team ran multi-channel campaigns via Meta, Google Ads and DV360. However, they lacked a reliable picture of which channel was really contributing to leads in the CRM. The platform’s own attribution from Meta, Google Ads and Google Analytics 4 provided contradictory data, as different channels claimed the same quote requests. Last-touch models could not reflect the contribution of brand awareness campaigns and CRM results were not visible for the advertising platforms. Cross-channel budget optimization was not possible under these conditions.

The result: 18.8 percent more online leads in CRM

The marketing team formulated the specific goal of optimizing online leads. The attribution dilemma could be solved by creating a fast, digital-first “performance truth” across walled gardens based on first-party data from the company’s own CRM. The prerequisite was good data quality: Successful attribution requires at least two years of historical data. Instead of pixels or cookies, bi-weekly spend and performance changes are used as controlled mini-experiments. The model learns the incremental contribution of each channel directly from the CRM lead data and not from clicks or platform events. This makes it privacy-compliant and independent of fragile user-level tracking.

In just a few months, Marketing increased its measured online leads in CRM by combining attribution, AI-based budget optimization and simulations. A reliable decision-making basis for the distribution of cross-channel budgets was created based on the company’s own data. The campaigns benefited from the faster decision-making cycles. Media planning was no longer static, but dynamic.

Thanks to the uniform attribution model for all channels, contradictory key figures were a thing of the past. And finally, attribution and optimization were combined on a single platform. Romina Knaus, Digital Media Manager at Generali Austria, emphasizes: “With Nexoya, we generated 18.8 percent more online leads thanks to the innovative attribution method. Nexoya’s data-driven insights put an end to speculation and help us to improve our performance.”

Guest author: Roger Gatti

Product strategist Roger Gatti has been VP of Product and Marketing at Nexoya AG in Zurich since 2024. Previously, as Chief Product Officer at 1plusX, he led the development of the AI-based data management platform and worked in product management at the Swiss financial technology provider Crealogix as well as at Google and Swisscom.

Read also: How insurers can reconnect with brokers in the digital age

On March 16, I spoke as an entrepreneur and AI expert at the United Nations in Geneva about a topic that is increasingly becoming a key issue in our digital society: Trust. His central thesis is as simple as it is alarming: it is not the lack of information that is endangering human rights today, but the loss of trust in information.

The problem today is not access to information, but the ability to trust it. A new infrastructure for transparency on the internet is needed to safeguard democracy, markets and social cohesion.

Trust as a new key resource

We live in an age in which information is available everywhere and at all times. But this abundance has created a fundamental problem: Trust has become the scarcest resource. It is no longer a question of whether information is accessible, but whether it is reliable. The loss of trust in content represents one of the greatest challenges for democratic systems and open societies.

A generation in the digital action space

This change is particularly visible among young people. In many regions of the world, they use digital platforms not only for communication, but also as tools for social change. They are organizing themselves, exposing grievances and campaigning for transparency and human rights. At the same time, they are faced with the increasing difficulty of classifying information correctly. The boundaries between reliable sources and targeted manipulation are becoming blurred.

The role of artificial intelligence

Artificial intelligence can play a decisive role in this context. It is able to analyze large amounts of data, identify patterns of disinformation and support journalistic processes. Used correctly, it can help to increase transparency and rebuild trust. At the same time, it must be clear that AI must not decide what is true. Its task is to support people in their ability to make judgments, not to replace them.

A structural deficit of the Internet

A central point of criticism concerns the architecture of today’s internet. Technical standards such as HTTPS have ensured secure connections worldwide. It is technically designed to disseminate information efficiently. What is missing is a comparable system for evaluating this information. While security standards have been established to guarantee the integrity of connections, there is no global mechanism to make the quality of content transparent. This gap is increasingly becoming a risk for society, business and politics.

The Global Trust Layer

To address this deficit, a new layer is needed on the internet: a global trust layer. This infrastructure should not control or censor information, but rather contextualize it. It should make visible where information comes from, how trustworthy its sources are and whether there are any indications of manipulation. The aim is to enable users to make informed decisions without restricting their freedom of choice.

Governance as a shared responsibility

A key question is who designs and controls such a system. Trust can only arise if responsibility is broadly distributed. An international model is needed that involves stakeholders from civil society, journalism, science and technology. Open standards and transparent processes are crucial to ensure legitimacy and acceptance.

Transparency instead of censorship

A global trust layer should not be confused with censorship. It is not about suppressing information, but about classifying it. People should continue to have free access to information, but supplemented by context that helps them to better understand and evaluate content. Transparency thus becomes the counter-model to control.

A new standard for the Internet

The challenge is to establish a global standard that is as effective as technical security protocols. Such a standard would not restrict the dissemination of information, but would make its quality visible. The Internet has given everyone a voice, but there is no system to categorize these voices. If we want to secure trust as the foundation of our digital future, we need to rethink the infrastructure of the internet.

An alternative is open models along the lines of the World Wide Web Consortium or the Mozilla Foundation, which focus on transparency, openness and community control.

Guest article by Bejan Choschnau

Bejan Choschnau is an entrepreneur and AI expert. He advises international authorities and organizations on strategic issues related to artificial intelligence, from technological development and digital infrastructures to societal impact and innovation. His work operates at the intersection of technology, policy and global information systems.

Read also: How Aiana is building Switzerland’s first AI-powered broker

The insurance landscape is changing rapidly. Products are becoming more interchangeable, price transparency is increasing and direct insurers are penetrating the market ever more aggressively with digital solutions. This raises an existential question for brokers: how do you remain relevant when pure price comparison has become the standard?

Platforms such as thebrokernews.ch make a decisive contribution to the industry here by promoting discourse on new business models and professionalization. They show that the broker of the future is no longer just a “risk buyer”, but must become a strategic risk manager. Those who still define themselves exclusively in terms of the lowest premium will lose access to management in the long term.

The vision: risk management must be affordable for SMEs

Until now, there has been an enormous barrier to risk management for SMEs, and especially for smaller companies: the cost. Anyone looking for a professional software solution today often encounters annual license fees in the high four or even five-digit range. This is simply not affordable for an SME.

This is where Marco La Bella’s vision comes in: he was convinced that professional risk management should not be a privilege for large corporations. It must be affordable, understandable and practicable for SMEs without academic baggage. I fully share this vision, and riskAware was born out of precisely this idea. It is about driving forward the democratization of risk management and providing brokers with a tool that also makes economic sense for their clients.

Dialogue at eye level: understanding risk instead of selling products

A structured but pragmatic approach closes the gap that many SMEs have not yet been able to fill. It is not about creating new complexity, but about asking the right entrepreneurial questions:

• What unexpected events could jeopardize liquidity tomorrow?
• Where do the costs of a loss exceed the capacity of the balance sheet?
• Which risks are consciously accepted and which are merely suppressed?

Such discussions fundamentally change the perception of the broker. He is transformed from a cost factor into a sparring partner who shares entrepreneurial responsibility.

Partnership approaches: Methodology and AI as “enablement“

In order to accomplish this role change in everyday broker life, efficient tools are needed that do not create any hurdles. The aim of riskAware is genuine “enablement”: The broker does not have to become a certified risk engineer, but needs a system that quickly visualizes and prioritizes risks.

Modern approaches take advantage of the benefits of digitalization. Today, AI-supported analyses help to run through scenarios in real time and professionally prepare the basis for decision-making. This saves time and raises the quality of advice to a level that was previously only achievable with an extremely high level of manual effort.

Integration into the broker process

Seamless integration into existing processes is a decisive factor for success. Risk management should not be an “additional project”, but should form the foundation of the annual customer audit. Instead of just checking sums insured, the focus is broadened:

1. Identification: What has changed in the customer’s business model?
2. Evaluation: Are organizational measures perhaps more efficient than insurance?
3. Decision: Which risks remain with the customer, which are transferred?

This transparency creates a relationship of trust that cannot be replaced by any direct insurer’s algorithm. The broker remains the central person of trust, strengthened by independent methodological support.

Differentiation through real added value

The path to modern risk consulting is not a hurdle for brokers, but an invitation to professionalization. Through the combination of personal customer proximity, an affordable and modern methodology through riskAware and the professional exchange in the community of thebrokernews.ch, brokers position themselves where they belong: as indispensable consultants at C-level.

The result is not short-term deals, but reliable mandates, deeper customer loyalty and a positioning that can no longer be attacked on price alone.

Would you like to take the next step towards strategic risk management? The vision of affordable and effective risk management is now a reality. Let’s talk about how you can make this added value visible for your customers: pragmatically, in partnership and at eye level.

A guest article by Fabian Germann

Fabian Germann is a sparring partner for decisions under uncertainty. As an independent risk consultant, he supports entrepreneurs and managers in understanding risks and making conscious decisions. With his approach, he makes risk management tangible for SMEs and shows insurance brokers how to use this expertise as a strategic lever.

Read also: “The broker becomes an entrepreneurial risk manager for SMEs”

Insurance, a well-established and time-tested risk transfer tool, remains a distant prospect for several emerging and frontier markets. Despite being among the most exposed to climate, health and economic shocks, individuals, businesses and even states in emerging markets often lack access to meaningful insurance coverage. This protection gap is not just a statistic but the source of deep and systemic vulnerability and must be addressed with understanding of the different dynamics of these markets.

Let’s try and unpack this complex issue and build a case for scalable inclusive insurance in emerging markets.

The Problem of high risk, low insurance

In many emerging economies, insurance penetration is painfully low such as 0.4% in Nigeria, 0.6% in Egypt and 0.7% in Pakistan to name a few. This is not just an inconvenience but a deep-rooted structural problem. When a flood submerges properties and infrastructure, when a drought destroys crops or when an income source vanishes, uninsured people are forced to fall back on themselves or informal communal support mechanism marked with strong family or tribal support systems. Those systems are in a way informal risk-sharing tools and have merit but often lack the scale, capital and speed to respond to escalating risks such as those brought by climate-driven catastrophes or health-related crisis.

On a macro level, the lack of formal insurance and the resulting protection gap limit the state’s ability to manage risk proactively. This ex-post approach places significant fiscal pressure on governments and ultimately slows economic growth. A case-in-point is Pakistan Floods 2022 which caused total damages of over $14.9 billion and total economic losses of around $15.2 billion. The country challenged with fiscal pressures, has a small non-life insurance market of roughly $850 million and almost non-existent disaster risk insurance, is relying on own resources and donors support to rehabilitate tens of million population and rebuild the damaged infrastructure.

Western models may not always translate

It is natural to look to mature Western markets for lessons. After all, those markets offer decades of experience and best practices, regulation, capital and innovation. Yet, trying to directly transplant their models into emerging markets often fails to address the core issue i.e. the different context.

Take Pakistan, for instance. Despite mandatory requirement of third-party liability motor insurance, the overall motor insurance penetration remains under 3%. One may term it as an enforcement problem but another view is that over 97% of motorists are effectively ‘rejecting’ a model of insurance that may feel alien, expensive or overly complex to them. This is not just a failure of enforcement but a bigger problem of product mismatch to the market.

The challenge, therefore, is not simply scaling existing insurance models but rethinking what ‘insurance’ means for these societies.

Reimagining insurance for emerging markets

Our argument is that in order to democratise insurance in emerging markets, we need a shift in approach and redefine the traditional concepts. That means shedding traditional mindsets, constructing new business models, redoing products, simplifying processes and redefining success or what we call ‘Inclusive Digital Insurance’. Two powerful tools help do this:

First is an ‘Insurance Inclusion’ mindset. This is not just a buzz phrase but a philosophy. Inclusion means starting from the perspective of those who have never or rarely interacted with formal insurance. When over 95% of a population lacks prior insurance experience, you cannot build on legacy models alone. You need to design from the ground up.

Second is, of course, the use of technology including frontier technologies. Looking at other industries around us, we learn that technology can actually help democratise a product or service in one lifetime. In our Inclusive Digital Insurance model, technology is not deployed as an enabler but forms the basis of market-creating innovation. By reengineering every step be that product, processes, distribution, payment, servicing, claims and engagement, we can reach more people and more efficiently, keep costs low and build trust.

Armed with the core concept of Inclusive Digital Insurance, let’s now understand how best it can be applied to expand the size of insurance in a particular emerging or frontier market.

Four socioeconomic tiers, four different approaches

Let’s break down a typical emerging market into four income segments. These segments must be viewed from the lens of emerging countries where per capita income often ranges between $1,000 to $2,000. Each requires a different strategy, approach and design.

Above per capita income or those making over $32 per day: They would loosely resemble a typical customer in developed markets. The infrastructure for traditional insurance exists here and incumbents are often already serving them nearly effectively. For these customers, technology improves experience such as telematics for motor, self-serving claims portals, AI-driven customer bots and so on. The use of technology is mostly about convenience and improved experience.

Middle mass or those making $8–32 per day: This is the largest and most critical segment for unlocking scale and presents an interesting challenge: Too poor to be sold conventional offerings and too rich to be ignored! This demographic now being fast dominated by digitally savvy Gen Z and needs commercially viable insurance that is just-enough, readily available, easy, relevant and frictionless – somehow the way they use other services like hailing a cab, online shopping, ordering a pizza or paying utility bills. This buyers’ group is really the face of ‘post’ post-COVID digitalisation in a typical emerging market and take technology for granted to simplify enrolment, premium (and claim) payment, servicing and claims.

Lower income segment or those roughly making $2–8 per day: Here, a hybrid model is required which is a blend of commercialisation and subsidies. A promising design is a ‘freemium’ model where a basic level of protection is offered at no cost to establish trust and awareness followed by paid cover continuation or upgrades. Digital tools keep overheads minimal ensuring most of the premium goes to coverage cost instead of administration and overheads.

Bottom of the pyramid segment or those making under $2 a day: For this segment, insurance must be viewed as infrastructure and public safety net as opposed to a retail offering. Risk protection in health, climate shocks or income loss should be state-backed and potentially free until people move out of extreme poverty. Too often, development programmes push regressive subsidy insurance schemes hoping that the community will be able to pay premium after certain number of years but we are yet to see meaningful success in that.

Emerging markets present strong opportunity

This is not only social initiative but a strategic opportunity as these markets offer significant commercial potential. There is a compelling business case for established insurers, reinsurers and brokers especially those from Western markets to engage with emerging markets.

Many emerging markets now have micro-insurer licensing regimes requiring surprisingly modest paid-up capital, as low as $30,000 in certain markets. This opens the door for lean and mission-driven players to pilot inclusive models. Naturally, this is easier said than done as the real challenge lies in building appropriate business models and executing them well. Without rethinking how to reach these populations or simply replicating Western models, the industry risks perpetuating low penetration and unserved customers.

Conclusion

The insurance industry stands at a crossroads. If we stick to the established models from the matured markets, we risk perpetuating exclusion, vulnerability and limited reach. But if we embrace a new mindset marked with inclusion and technology for scale, we can redefine what insurance means in the emerging markets.

Rehan Butt

Rehan Butt is the founder & CEO of Instaful Solutions and works on democratising insurance in emerging markets by engaging with private, public and development sectors. His LinkedIn profile.

Read also: thebrokernews Podcast Episode 6: Newton Bezeng and BeNew Insurance’s mission to revolutionize microinsurance in Africa