Asmir Imeri, founder and CEO of S.O.I. AG, relies on a combination of a digital brokerage platform, exclusive financing solutions and a dedicated learning world to train independent financial and insurance advisors to become genuine mortgage specialists.

In an interview with thebrokernews, he talks about the challenges in the mortgage market, his path from construction drawing to the financial sector and why brokers need to rethink their role now more than ever.

Mr. Imeri, you started with an apprenticeship as a draughtsman and then moved into mortgage brokerage via the finance and insurance sector. What was the decisive moment for you to change this chapter?

When I started working for an asset management company in 2012, I realized that the demand for home ownership and mortgage advice would increase significantly over the next ten years. I wanted to be part of this development and build something of my own that would intelligently meet this demand.

In April 2021, you founded S.O.I. AG with the vision of connecting the world of financial and insurance advisors with the world of mortgages. What gap did you identify in the market back then?

I have seen that many financial and insurance advisors would also like to offer their clients mortgage advice, but they lack the expertise, experience and access to mortgage lenders. Today, we are closing this gap by offering them know-how, technology and a network.

Your “Chameleon-H” platform automates many steps of the mortgage brokerage process: from checking affordability to submitting a dossier. What specific changes does this technology bring for brokers?

Agents can process recurring work steps efficiently and use their time more productively. The platform helps them to serve more customers and increase the quality of advice at the same time.

You talk about seeing brokers not just as lead generators, but as genuine financing partners. What does that mean in concrete terms and how does it change the business model for brokers?

I don’t want advisors who simply pass on their clients as “leads”. There is usually a long-standing relationship of trust between advisor and client and this should be maintained. The advisor continues to look after their client directly, with our support in the background. Customers really appreciate this because they know that their advisor is really acting in their interests.

A central element of your strategy is the training of mortgage specialists via your learning world. Why is this training more important today than ever before?

Because expertise enables quality. I am convinced that the market will consolidate strongly without sound knowledge, and that is a good thing. Anyone who brokers mortgages should understand what they are doing. Otherwise it’s like typing in a mathematical formula without knowing that “dot before dash” applies.

In the “Family Office Mortgages” area, you serve sophisticated construction and real estate projects with more complex financing structures. How does this special financing differ from traditional mortgages and what role does S.O.I. play in this?

In the case of construction and real estate projects, the term is significantly longer, the volumes are higher and more parties are involved. In addition, financing is often provided in several tranches over different phases. We see ourselves as a conductor who ensures that every note is played at the right time.

The Swiss mortgage market is facing several challenges: rising customer expectations, higher regulatory requirements, digitalization. From your perspective, which two or three trends will shape the market the most in the next 2-3 years?

I am convinced that customers will increasingly approach brokers directly to finance their mortgage. At the same time, the proportion of those who want to handle their financing independently using digital tools will rise.
In addition, the shortage of skilled workers and high wage costs mean that repetitive processes are being automated systematically, and the technical possibilities for this have long been available.

You work with a broad network of banks, insurance companies and pension funds. How do you ensure that these partner networks deliver real added value for both brokers and end customers?

Loyalty and trust, coupled with a shared understanding of quality and strong implementation skills – that is our recipe for success. This significantly reduces idle time and increases the success of the deal.

We are also very careful in our dealings with our financing partners: we don’t ask for five offers at the same time, but usually one or a maximum of two. The added value of a third or fourth offer is practically zero. Unfortunately, there are brokers who do exactly that – supposedly in the interests of the customer. In reality, however, they put the needs of the mortgage lender in the background and weaken trust in the entire process. Our approach is different: focus, fairness and efficiency – this is how we create sustainable added value for everyone involved.

Many brokers are complaining about falling margins and increasing competitive pressure. How are you responding to these market conditions with your offering?

We give every broker the opportunity to generate higher income in the mortgage market with their existing customers. The price for this is further training in line with my motto: “No brokerage without further training.”

When you look back on the founding phase of S.O.I.: What were the biggest stumbling blocks and what experiences proved particularly valuable?

One challenge was to overcome the “professional blindness” of the traditional mortgage world and to consistently rethink the loan application process from an IT perspective. It was particularly valuable to realize that when day-to-day business and technology mesh perfectly, innovation knows no boundaries.

Sustainability and efficiency are becoming increasingly important in the financial and real estate sector. Does S.O.I. have corresponding approaches, e.g. sustainable financing or energy-efficient real estate projects?

Yes, definitely. The issue of sustainability is coming from all sides today: Customers, developers and financing partners. Our task is to reconcile the expectations of mortgage borrowers with the offers of mortgage lenders.

What is your roadmap for the next five years? What milestones are you aiming for with S.O.I. in terms of growth, technology and market positioning?

After reaching break-even and the proof of concept, we are now in the growth phase and are preparing for scaling. Our goal is to build the largest freelance mortgage team in Switzerland and further consolidate our position as a center of excellence for mortgage financing.
We can use the data and experience we have gathered to build on AI-based systems in the future. I can already say today that we are working on something that will play a decisive role in shaping the future of the mortgage market.

Finally, what advice would you give to a financial or insurance advisor who wants to enter the mortgage market today and what should they pay particular attention to?

Set your sights on long-term customer loyalty, continue to educate yourself and start with your existing customers.

And never forget: Every year, around CHF 100 billion in mortgages are refinanced or refinanced in Switzerland – the market is huge for all those who work competently and consistently.

The questions were asked by Binci Heeb.

Read also: Home ownership with risk: every second buyer uses a pension fund

Anyone who brokers cyber policies today must understand and quantify risks and accompany them throughout their entire life cycle: from the initial assessment to systemic control. This is precisely where Maxime Cartan, CEO of cyber intelligence specialist Citalid, sees the crucial role of insurers, brokers, and technology partners: they are becoming orchestrating players who bring together data, models, and prevention knowledge.

Thebrokernews talks to the winner of the Swiss InsurTech Hub Summit & Awards 2025 about how brokers are evolving from sellers to strategic entities that curate risk-based solutions, influence capital allocation, and build trust between companies and insurers. Because cyber is no longer a product category, but a dynamic risk class that needs to be managed continuously.

What do your collaborations with insurers, brokerage firms or technology partners look like? What role must intermediaries and brokers play in the shift towards data- and risk-oriented cyber policies?

We collaborate across the entire cyber insurance policy lifecycle, from initial screening to systemic risk steering:

1. Risk selection & awareness

With our fully automated, lightweight assessment, underwriting teams can quickly evaluate a company’s alignment with their risk appetite without any client interaction. It enables faster triage at scale and more efficient distribution pipelines.

2. Underwriting & renewal

At the deeper assessment stage, brokers and insurers can combine multiple signals (external and internal, cyber and financial, sourced from Citalid or other technology partners, …) to structure guarantees and pricing based on actual exposure, not generic maturity scores. This reduces uncertainty and improves both margin and client fit.

3. Prevention & risk management

For critical or complex clients, brokers and insurers can equip them with our full cyber risk management platform. CISOs and risk teams can then analyze their exposure in depth, run scenario simulations, and optimize both security investments and insurance coverage. This creates a true risk-reduction loop: the insured becomes more resilient, and the carrier’s exposure improves over time.

4. Portfolio resilience

At portfolio scale, we identify accumulation vectors, monitor threat evolution and concentration, and help carriers refine underwriting guidelines to stay aligned with risk appetite. This is essential in a world where threat actors evolve quickly, and where supply-chain and cloud dependencies create systemic vulnerabilities.

5. Cyber CAT simulations

We provide catastrophe modelling services at the portfolio level to simulate extreme but plausible events. This enables insurers and reinsurers to stress-test strategies, negotiate reinsurance, and challenge assumptions around capital management.

Ultimately, intermediaries remain essential. Their role is evolving from “selling policies” to curating risk-informed solutions supported by analytics, because this is what clients now demand. This is why we see more and more brokers either developing internal cyber quantification capabilities, or partnering with pure-players like Citalid to accelerate time-to-market and credibility.

Cyber risks are becoming increasingly relevant not only from a technological perspective, but also from a regulatory perspective (e.g. operational resilience, data protection). How do you integrate regulatory requirements into your solutions, and how do you build trust among companies and insurers?

Regulation is converging toward a single message: prove that you understand your first- and third-party cyber risks, both on a strategic and operational standpoints.

We help our clients meet the requirements of many frameworks such as: DORA, NIS2, GDPR, sector-specific resilience standards.

Trust comes from transparency: our models are explainable, consistent, and backed by both cyber and actuarial methodologies. We also undergo regular audits from clients and partners.

Your platform not only recommends risks, but also generates an “investment roadmap”, i.e. preventive measures, according to your website. How important is prevention in relation to pure damage or risk modelling, and how do you measure its effect?

Prevention is central, especially when speaking to decision-makers who understand financial impacts far better than technical metrics.

It’s not enough to tell a company “you have €20 million of cyber risk”. You must tell them:

• which actions reduce it,
• by how much,
• and with what financial ROI.

That’s why our investment roadmaps go beyond prevention. They map recommendations across the full spectrum of cyber exposure: prevention and identification, protection, detection, response, resilience and recovery, and even the optimization of insurance coverage.

Every recommendation in Citalid is tied to an expected loss reduction curve. In other words, customers see the financial effect of a mitigation before implementing it. This changes the budget discussion dramatically: instead of debating tools or compliance, executives allocate capital based on impact, efficiency and return on risk reduction.

What technological trends do you see today that will be crucial for cyber insurance and risk management in the next 2–3 years? Keywords could be: AI-driven automation, real-time threat intelligence, embedded insurance, etc.

I would say that, given what we see today, four trends are likely to be crucial in the upcoming years:

1. AI-driven automation of underwriting and exposure assessment

From submission triage to data ingestion and scenario modelling, AI will accelerate underwriting workflows and enable more flexible products, including parametric models. Automation is not about replacing people, it’s about freeing experts to focus on judgement, risk appetite, and capital allocation.

2. Real-time risk intelligence integrated with pricing

Cyber will move closer to a “live” insurance line: pricing and conditions informed in real-time by the evolving threat landscape, not static questionnaires. This will turn cyber insurers even more into true risk-reduction partners, because risk posture becomes a shared, continuously updated metric.

3. Embedded cyber insurance inside digital and cyber ecosystems

Security controls and insurance will be bundled into the tools companies already use, such as cloud platforms, cyber solutions, payment providers, IT services. Coverage and resilience may become native to the technology stack rather than a separate procurement cycle.

4. Probable coverage of risks generated by massive GenAI adoption in companies

Generative AI is creating new risk scenarios: model poisoning, misuse, hallucination-driven decisions, IP/data leakage, … which are adjacent to cyber and face similar modelling challenges. Insurers will have to cover AI-enabled incidents long before actuarial datasets exist. The players who can quantify these risks through expert knowledge, contextual intelligence and dynamic modelling will gain a decisive advantage.

In short, the winners will be those who combine automation with deep cyber intelligence, and who are willing to put skin in the game… aligning product economics with the real risk their clients face when things go wrong.

As a young company in a highly regulated and risk-sensitive area: What were the biggest challenges you had to overcome, e.g. in data acquisition, sales, scaling or vis-à-vis established market participants?

Entrepreneurship is the story of trying to overcome challenges… sometimes with more success than others, but always with resilience. A few come to mind (and this is by no means exhaustive):

• Trust building with CISOs first, then insurers — both require time, proof, and deep due diligence.
• Mindset shift in markets used to actuarial modelling on long historical datasets: introducing new ways of modelling new risks is hard, especially in soft market.
• Data acquisition: cyber data is sensitive, multidimensional and hard to standardize. Our background in threat intelligence at the French cyber defense agency helped us build the right methodology and validation pipelines.
• Long sales cycles, particularly with large banking and insurance institutions.
• Scaling internationally while being disciplined with capital and resources.

Winning major deals and strategic partners helped accelerate the trajectory, but only after we earned it.

Do you work primarily with large companies, SMEs or directly with insurers? What is your ideal customer group, and why?

We operate across three segments:

• Large corporates directly, because their exposure is high, global, and they already understand the value of quantifying cyber risks. From the ones I can publicly name are industry global leaders in their respective segments such as Alstom, Lagardère, Fresenius…
• Insurers, reinsurers and brokers, who leverage our Portfolio product to steer underwriting, accumulation and systemic exposures.
• SMEs indirectly, via partners, insurers and brokers, to help overcome the awareness and distribution challenge in this segment.

On the corporate side of things, our ideal client is one who aligns cyber risk with broader financial decisions: insurance capacity, capital allocation, vendor dependency, operational resilience. We can usually tell very quickly by looking at who is in the room, whether it’s only the CISO, or whether the Risk Manager, business owners and CFOs are present and genuinely willing to understand the exposure and make proactive decisions.

This is why we often use the phrase: “Dare to know, be ready to act.” Because once the risk is framed at that level, quantification becomes a necessity, not a luxury.

What values drive your team at Citalid? How do you create an innovation- and growth-oriented corporate culture, especially when entering the international market?

One of our employees once said, “Citalid is a company where values aren’t written on a wall… you just feel them every day.” That stayed with me. If I had to name them, I would say:

• Trust: we never compromise on modelling quality or transparency. In an industry built on uncertainty, credibility is earned through rigour.
• Pragmatism: our job is to translate complex cyber signals into clear decisions, grounded in actionable financial metrics. We are allergic to buzzwords.
• Collective intelligence, my former field of research: it is often misused today but, under the right conditions and environment, we are smarter together. And I include employees but also clients, partners, insurers, regulators. Cyber risk is systemic; the way we tackle it must be collaborative.

I believe these values naturally shape our culture as we expand internationally. Innovation is not a department at Citalid: it’s a collective discipline, anchored in humility, curiosity and a strong sense of measurable impact.

Looking five years into the future, what is your vision for Citalid and the cyber insurance industry? What role will your company play in this?

Five years from now, I see cyber becoming as structured as natural catastrophes: with standardized exposure metrics, accepted accumulation models, benchmarks shared across the industry, and eventually capital markets instruments. Cyber will move from being an uncertain “technical topic” to a genuine asset class of risk.

Our ambition is for Citalid to be the global reference infrastructure for quantifying and managing cyber risk, for insurers, banks, and large enterprises. The missing layer that allows the market to track, price, transfer and steer cyber exposure with the same clarity as any financial asset.

When that layer exists, the impact is not only financial: better intelligence and better models lead to better decisions, which ultimately create a safer, more resilient digital economy.

Maxime Cartan Maxime Cartan is the co-founder and CEO of Citalid, a tech scale-up recognized as a European leader in Cyber Risk Quantification (CRQ). He previously worked as a cyber threat intelligence specialist at ANSSI, France’s national cybersecurity agency. A graduate of both a top engineering school and a top business school in France, he holds certifications in offensive security (OSCP, CEH). Before joining ANSSI, Maxime was a partner at Hypermind, a startup focused on predictive geopolitical analytics.

The questions were asked by Binci Heeb.

Read also: Cyber risks as a financial investment: Citalid’s vision for the future of the insurance industry (part 1)

Maxime Cartan, CEO of Citalid, explains how cyber risk is becoming one of the most strategic and scalable opportunities in the insurance industry. His company transforms cyber threats into measurable financial metrics, enabling insurers, corporates and financial institutions to understand, assess and manage digital risk as an asset class.

Thebrokernews talks to the winner of the Swiss InsurTech Hub Summit & Awards 2025.

Maxime, please tell us briefly: How did Citalid come about, and what was your personal motivation for founding a company that quantifies cyber risks?

Back in 2017, my co-founder Alexandre Dieulangard and I were both working in cyber threat intelligence at ANSSI, the French cyber-defense authority. We were helping compromised critical organisations, both governmental and private.

And we kept running into the same wall with executives: every strategic decision ultimately comes down to economics, yet cyber risk was still discussed only in technical terms. The bridge between cybersecurity, business continuity, financial exposure and insurance simply didn’t exist. Organisations were essentially flying blind.

Alexandre and I have always been driven by entrepreneurship, with complementary backgrounds: his legal and geopolitical lens, and my engineering and mathematical approach to cyber. Building a concrete, silo-breaking solution felt like the natural next step. We wanted to answer one simple question: “What is the financial exposure of my company to cyber risk, and what should I do about it?”

Our motivation was clear: make cyber risk measurable, comparable and actionable for executives, insurers and financial institutions. Because once you can quantify it, you can finally manage it.

What exactly distinguishes your platform from traditional approaches to cyber insurance or cyber risk management? What technological levers do you use ? AI, threat intelligence, modelling?

Traditional approaches rely on external scoring or questionnaires. You’re forced to choose between speed and substance: long questionnaires capture internal posture and business context, but are time-consuming and painful; external scans, while fast and necessary, only reveal technical signals visible from the outside.

Meanwhile, cyber is a dynamically evolving risk. The limited historical loss data available becomes obsolete quickly, yet cyber remains one of the top risks for corporates and insurers. The industry needs a new approach that merges threat intelligence, business context, external and internal signals, and financial impact into a single coherent view… without compromising speed.

Citalid is now the leading cyber risk quantification technology in Europe because it addresses this gap through three pillars:

• Live cyber threat intelligence contextualized to specific sectors, geographies and offensive scenarios.
• Advanced modelling, mixing Bayesian networks, Monte-Carlo simulations and financial impact expertise.
• AI systems that automate analysis, correlate signals and detect risk propagation across IT systems and company portfolios.

The outcome is a platform that goes far beyond maturity assessments. It computes incident likelihood, expected losses, worst-case scenarios, and the ROI of security and insurance strategies. In short: we translate cyber into the language of risk, capital and insurance, making it measurable, comparable and decision-ready.

You won the Swiss InsurTech Hub Summit & Awards 2025 with the statement that cyber is the “most profitable and scalable sector of the next decade”. Can you explain this in more detail, and how do you see the role of insurers in this?

Yes… or at least it should be, if we collectively build the right foundations!

Cyber is the only insurance line where underlying exposure is growing faster than capacity, modelling capabilities or underwriting expertise. Digitalization, AI adoption, cloud concentration and hyper-connected supply chains are creating exponential exposure. And yet we still see huge coverage gaps: limited capacity for large corporates, and limited market penetration for the mid-market.

This combination creates a perfect storm of opportunity:

• Tremendous and accelerating demand
• Limited available capacity
• A critical need for better pricing, modelling and risk selection

Insurers who truly master cyber today will shape the market for the next decade. I’m convinced cyber will become a core strategic line of business, on par with property or specialty but far more scalable, because it underpins every modern organisation.

The role of insurers is pivotal: not just to provide capital, but to drive resilience, improve security incentives, and help the entire economy understand and price digital risk with the same clarity as any other financial exposure.

After winning the 2025 Awards what are your next growth steps? In which markets do you want to expand? Which partner ecosystems are important to you?

Three priorities come to mind for our next phase of growth.

1. Geographic expansion

We already serve clients in a dozen countries, and our focus areas are Switzerland, Germany, the UK and North America. These markets combine high cyber exposure, mature insurance ecosystems and strong regulatory drivers.

2. Deep integration within partner ecosystems

Brokers, insurers, reinsurers, MGAs and GRC underwriting platform vendors are central to our strategy. Our aim is to make cyber risk quantification natively available where underwriting, risk selection and capital decisions are made.

3. Scaling the product footprint.

We’re expanding our portfolio-level analytics for insurers and banks, especially around risk accumulation, systemic scenarios and the connection between cyber and credit risk.

Switzerland is a strategic hub for us: highly mature, innovation-friendly, and globally connected. It’s an ideal launchpad for international expansion.

In your opinion, what are the biggest obstacles that still prevent insurers from underwriting cyber risks in a data-driven way or assessing them accurately?

Cyber risk presents a combination of obstacles that makes it one of the biggest actuarial challenges insurance has ever faced.

A young risk with limited, inconsistent historical data

There is no century-long loss history or universally accepted taxonomy. Without common definitions, benchmarks and exposure metrics, insurers struggle to calibrate models and write policies.

Extremely high volatility

Attacker behaviour, tools and incentives evolve at digital speed, making past loss data a weak predictor of future events.

A human-driven, geopolitically sensitive threat

Cyber risk is shaped by malicious intent, geopolitical tensions, economic cycles and emerging technologies — all factors that introduce deep uncertainty.

A technically complex domain with scarce expertise

Understanding vulnerabilities, controls, architectures and behaviours requires expertise that is rare… and in a tense hiring market, extremely hard for insurers to recruit and retain.

All these factors lead to a lack of appropriate solutions on the market built to help insurers grow cyber insurance portfolios profitably and optimize underwriting processes based on a strong cyber expertise.

Citalid helps overcome these gaps with standardised exposure metrics, live attacker-centric intelligence, and automated modelling that integrates natively into underwriting and risk workflows, making cyber quantifiable, comparable and insurable at scale.

How do your corporate customers specifically experience the benefits of your solution? Do you have any examples of how risks or premiums have changed?

Our cyber risk quantification engine actually powers two complementary products:

• Citalid Portfolio — large-scale third-party risk quantification used for insurance underwriting, portfolio accumulation analysis, supply-chain risk management (TPRM), and even credit risk evaluation.
• Citalid Core — detailed first-party risk modelling that helps corporates identify their most relevant scenarios, quantify exposure, and run what-if simulations to build an optimal security and insurance roadmap.

Together, they create a virtuous risk-reduction loop: Portfolio users (insurers, brokers, financial institutions) are incentivized to co-sell Core to their most critical clients, because improving the insured’s risk posture directly reduces their own exposure. It’s a true win-win dynamic.

Corporate users of our Citalid Core product usually report three concrete benefits:

• Radical visibility: for the first time, executives understand which scenarios matter and the financial magnitude of each, in clear business language
• Optimized premiums and policy terms: some clients have reduced premiums by up to 20% or renegotiated conditions to accurately reflect their true risk, strengthening trust with their insurer
• Smarter investment decisions: instead of “buying everything,” they prioritize security actions with the highest marginal risk-reduction impact, benefiting every stakeholder involved

And on the insurer side, the value is symmetrical: one carrier used our models to reprice an entire portfolio after identifying clusters of companies with disproportionately high projected loss ratios… a pattern they simply couldn’t detect before.

Cyber insurance traditionally has the problem of having few historical claims records. How do you deal with this? How do you model scenarios that are rare but still possible?

Traditional actuarial modelling of risks requires large, stable historical datasets… which simply don’t exist in cyber. That’s why we rely on Bayesian networks, and just to be clear: no, I’m not talking about the yacht called “Bayesian” someone from the audience mentioned to me after the Awards!

In simple terms, a Bayesian network is a kind of explainable AI that combines expert a-priori knowledge witha-posteriori real-world observations.

On the one hand, we codify what cyber risk experts already know about attackers’ victimology, behaviours, techniques and defenders’ control effectiveness, likely business impacts and resilience. That becomes the prior: a structured, quantified understanding of how cyber incidents unfold, even before seeing any data.

On the other hand, every new incident, be it a ransomware campaign, a data leak, a supply-chain compromise, … provides new signals. Bayesian updating through inference allows the model to learn and recalibrate from every new data point, even if they are rare or incomplete.

This is crucial because cyber is the opposite of traditional actuarial domains: it’s adversarial, interconnected and fast-evolving. Waiting for decades of loss data is simply not an option.
Bayesian networks allow us to augment scarce datasets with contextual intelligence and cyber expertise, and then refine the model continuously as reality evolves.

The result is a genuinely new viewpoint for insurers: one that can model plausible but unseen scenarios, quantify tail events, and understand how risk propagates across technologies and supply chains. In other words, it gives actuaries a rigorous framework that finally matches the nature of the risk, which is why it resonates so strongly with carriers frustrated by the limitations of historical-only approaches.

Maxime Cartan is co-founder and CEO of Citalid, a technology scale-up recognized as the European leader in Cyber Risk Quantification (CRQ). He previously worked as a cyber threat intelligence specialist at ANSSI, the French national cybersecurity authority. He is a graduate of a prestigious engineering school and a renowned business school in France and holds certifications in Offensive Security (OSCP, CEH). Before joining ANSSI, Maxime was a partner at Hypermind, a startup company specializing in predictive geopolitical analysis.

The questions were asked by Binci Heeb. The second part of the interview will be published on Monday, December 1.

Read also: Start-ups, winners and strong ideas: The InsurTech Awards 2025

At the interface between satellite technology, geodata and insurance, Caelus.space develops solutions that can detect ground movements before damage occurs.

Founder Amit Hellman explains how the “space revolution” is changing risk modeling, why collaboration is important and what insurers need to know as new levels of data reshape underwriting and claims handling.

Amit Hellman, can you briefly tell us how Caelus.space came about? What problem did you originally want to solve and how did this develop into an InsurTech offering?

My connection to the world of geotechnics and remote sensing has always been one of my greatest passions. Over the last few years I have gained experience in development and management roles in the remote sensing industry whilst studying geotechnical engineering at university. I was constantly looking for a way to combine these two disciplines to make a positive difference.

As I continued this journey, I learned of a specific case in Israel where an entire neighborhood was slowly sliding down a slope, causing hundreds of millions of dollars worth of damage. This inspired me to develop a methodology, i.e. a thesis that combines my accumulated experience to determine whether this movement can be detected at a very early stage.

The initial results were very encouraging. We received numerous case studies from partners and organizations worldwide for validation, and in each case we were able to successfully observe and predict the developing damage months and even years in advance.

This profound insight into our predictive ability naturally led us to the insurance sector. Insurers can use these unique insights not only to expand their portfolios and manage risks more effectively, but above all to ensure the safety of their end customers.

Your platform focuses on the early detection of ground movements such as subsidence and land deformation. What technologies enable this predictive capability and how does it differ from traditional risk assessment?

Our forecasting capability is based on a robust, five-stage engine that masterfully integrates advanced sensor technologies. At its core, we utilize a constellation of over 29 satellites, relying fundamentally on Synthetic Aperture Radar (SAR) and multispectral imagery as our data foundation. The real innovation lies in the fusion of this powerful remote sensing data with geotechnics. On the one hand, we use machine learning models to extract numerous important geotechnical parameters directly from the satellite data. On the other hand, we use sophisticated signal processing to detect structural movements with exceptional accuracy down to just 2 mm for each structure we map.

By combining this precise, real-world data with a deep learning model, we have created an engine that is able to provide a prediction of evolving ground movement hazards for any building in the world. This approach fundamentally changes risk assessment from a reactive process based on historical loss events and visible damage to a proactive and predictive process that enables insurers to identify and mitigate risks months or even years before a catastrophic loss or claim occurs.

You started out in the infrastructure sector before expanding into the insurance sector. What triggered this strategic change and what key differences do you see between the two markets?

We started in the infrastructure sector and are still very active there with projects in the USA, Israel and even India. The main catalyst for the strategic shift to insurance was the realization that we had predictive capabilities. When we saw that we could reliably predict defaults months or years in advance, we knew that this insight could change risk management on a global scale and across the portfolio, which is exactly where the insurance industry operates.

Although both the infrastructure and insurance markets are showing increasing openness to new, disruptive technologies, the operating cycles differ considerably.

In the infrastructure market, things are generally more manageable. The infrastructure itself belongs to our direct customer, be it a technical manager, safety manager or maintenance manager. When we identify a risk, there is a clear chain of command and an immediate interest to act. If we identify a deformation that could cause 10 million dollars worth of damage to a major intersection, we don’t have to convince multiple stakeholders outside their operational area; they can simply act, for example by opening a drainage channel in a retaining wall. The sales cycle is therefore shorter, and the damage we prevent can often mean the difference between job security and a serious career setback for the responsible party.

The insurance world, on the other hand, requires patience and time to build trust and integrate into the company’s operations. We understand that. The people responsible for implementing our solution are part of a huge value chain. It takes time for a large company to understand exactly where in this chain the solution is needed and how big the problem is. For example, when we first approach departments such as NatCat or Business Development, they often do not have accurate data on the extent of the ground movement problem in their portfolio. Only after an in-depth analysis of their damage statistics do they realize the true extent of the problem. In addition, implementing new technologies in a large insurance company also requires a lot of patience, as they often work with multi-year work plans and the timing of a decision can unfortunately fall outside the time window for new strategic initiatives.

France suffered losses of over 1.2 billion euros last year due to subsidence, which led to a 20% increase in premiums. How can insurers use your findings to mitigate or even prevent such cost increases?

The situation in France is a prime example of how climate change is creating new, uncontrolled systemic risks for the insurance industry. France in particular has experienced record-breaking droughts and rainfall fluctuations due to climate change, which has led to an increased risk of ground movement. The €1.2 billion in losses is alarming, especially as the state does not classify land subsidence as a natural disaster (Nat Cat) in most cases, forcing customers to sue their insurers, who ultimately have to bear the costs.

The core problem is the lack of an accurate, continuous and scalable tool to understand where these losses occur. Caelus.space solves this problem directly by providing property-level predictive risk analytics across an entire national portfolio. We enable insurers to mitigate this cost increase in three key ways:

1. Portfolio-wide risk assessment: We calculate the specific risk value for each individual property in a country such as France. This allows the insurer to accurately quantify its expected annual portfolio losses due to ground movements for the coming year and move from aggregated models to precise financial projections.
2. Proactive loss prevention: This is our most powerful tool. We can identify emerging threats to the customer’s property months or even up to two years in advance. By pinpointing the exact location of the looming problem within the property, we enable minimal early intervention to prevent a potentially catastrophic six-figure loss.
3. Refined underwriting and risk transfer: If the client is unwilling to work with the insurer to mitigate the identified risk, our data provides the underwriter with the objective, detailed evidence they need to make informed decisions. This allows the insurer to exclude certain quantified sinkhole risks from the policy, avoiding liability risks before they materialize.

Many start-ups fall in love with their technology instead of focusing on the actual business needs of insurers. How do you ensure that Caelus.space remains customer-centric and not technology-centric?

After over 200 conversations with industry executives, we have gained a deep understanding of the sector’s needs: from who to approach, to understanding the annual decision cycle, to the urgent need to comply with regulatory frameworks and standards such as GDPR, DORA, ISO and SOC.

We quickly understood a central fact: The customer wants technological change, but the operational “ship” must continue to sail in its current direction without major disruption. Therefore, our approach is based on removing barriers to entry and integration.

We don’t need any internal data from the client to get started. We just need to know which country or portfolio they want to monitor. Then we apply our technology to provide the first level of data.

When we reach the proof-of-concept (POC) phase, we don’t hold back. We open the ‘hood’ fully and present all five stages of our model to ensure the client understands the depth of the product. We provide a building-level risk assessment as well as more than 15 additional statistics.

We recognized early on that setting up a complex CRM system for insurers is unnecessary and would only represent another hurdle. Instead, we adapt to the pace of the customer. We deliver the raw and processed data your teams need in the simplest and most accessible format: via a single API or via simple Geographic Information System (GIS) layers.

What does the current “space revolution” mean for the insurance industry? Which new data levels or functions will bring about the biggest changes in the next three to five years?

The current “space revolution” is an electrifying force that is profoundly changing the insurance industry. Driven by an unprecedented increase in orbital utilization with over 12,000 satellites orbiting the earth and at least 60 more being launched every week, the technological convergence of advanced AI and enhanced optics is fundamentally changing risk management. At the heart of this transformation are the approximately 3,000 active Earth Observation (EO) satellites that enable “hundreds of different types of monitoring and modeling” that directly benefit the insurance industry.

Let’s take a major disaster as an example: If a tornado sweeps through an area in the afternoon, the exact location of the damage and flooding can be determined and transmitted to insurers that same evening. While this instant, area-wide assessment capability is invaluable for speeding up claims processing and easing the burden on customers, the truly revolutionary aspect is the ability to use this continuous stream of data for predictive risk management.

Looking ahead over the next three to five years, the continued integration of EO data with powerful AI will bring about the most transformative new capabilities, enabling “any manipulation of our portfolio”. This means moving beyond general risk modeling to granular asset-level information. The space revolution is thus transforming orbital data into highly actionable, predictive information.

Caelus.space is currently self-funded. How does this affect your growth strategy, your timetable and your product roadmap compared to VC-financed competitors?

In a time characterized by rapid venture capital deployment and pressure for exponential growth, Caelus.space is taking a very different course. Our decision to remain bootstrapped is not a limitation, but a fundamental pillar of our strategy, timeline and product roadmap, especially in comparison to VC-funded competitors.

For us, patience is key. In a specialized sector like insurance technology, where trust and deep integration are paramount, an aggressive, VC-driven burn rate would simply lead to a waste of capital.

This approach has a direct impact on our growth strategy. Rather than pursuing aggressive, short-term sales targets, we focus on real market entry, building meaningful partnerships and growing our team in the right way. We are aware of the inherent difficulties and long sales cycles within our potential customers’ legacy systems. We avoid putting pressure on customers to speed up the sales process. Instead, we accompany them, understand their internal constraints and adapt our development to their specific needs.

Our product roadmap reflects this customer-oriented philosophy. We avoid the temptation to “reinvent the wheel” or pursue prestige projects. Our primary commitment is to iterative refinement and attentive listening. When clients predominantly request specific levels of data to be integrated into their legacy systems, we prioritize this. Our ongoing efforts are focused on continuously improving the accuracy and precision of our core engine, while working diligently to reduce maintenance costs wherever possible.

Ultimately, many startups boast about being the “best in the world” or win awards in quick succession, but this recognition often doesn’t reflect the actual pace at which trust is built and delivered with customers. We believe there are simple ways to reach the right people through quality engagement and leveraging strong partnerships. A VC model would inevitably lead to intense scrutiny over time and pressure for an early exit. For Caelus.space, this mindset is detrimental to the long-term health of our company, the quality of our product and the wellbeing of our team. We choose sustainability and trust over speed and exit pressure.

Can you name a pilot project or proof-of-concept (without confidential details) that has taught you something surprising about insurer behavior or underwriting culture?

One of our most insightful proof-of-concepts (POCs) involved a large insurer who wanted to assess the risk of collapse for a portfolio of very valuable, expensive buildings. Specifically, it involved retaining wall failures and potential landslides near these luxury properties.

Our analysis showed that the overall risk for the entire portfolio was low, but for one particularly critical building, our engine categorized the risk as medium. The reason: our geodata clearly identified an active landslide zone near the building, and the building itself showed a measurable reaction to this ground movement.

The surprising thing was not the technical identification, but the associated underwriting dilemma. The insurer presented our findings to their internal team of risk engineers. Our data was immediately of great value and saved them a lot of time as it accurately guided the surveyor to the active zone and allowed them to target their ground investigations.

However, when the insurer tried to obtain even the simplest information from the potential customer, such as the year of construction or any building certification standards, it was met with resistance. The customer’s attitude, which is common in the high-net-worth private customer segment, was essentially: “This is the situation. Get over it. Insure us or don’t bother.”

This POC highlighted the information asymmetry that insurers face. Without Caelus.space’s predictive insights, the insurance company would have remained blind to the significant, evolving risk in the region and may have ended up paying tens or hundreds of millions of dollars for a single claim on a luxury property. Our technology was the only objective tool they had at their disposal to make an informed, data-driven underwriting decision in the face of the client’s lack of cooperation.

You have participated in accelerator programs, the Global InsurTech program and the Israel InsurTech Hub. How important are these ecosystems for networking, learning and market entry?

To reach the right people and navigate complex foreign markets, connections are essential. We strongly believe in working with local ecosystems, especially the mentors embedded within them. For a company entering a new market, such as expanding from our home market into Europe, the best entry point is always through the established local insurance communities and centers. We have participated in two programs: InsurTech Israel and a combined program with InsurTech Germany. We are also active in the Swiss InsurTech Hub.

In each program, we met dozens of industry executives in various roles and gained a fundamental, practical understanding of how the insurance world works effectively. In particular, the InsurTech Israel community provided us with truly exceptional mentors and a powerful network that was critical to our growth. With the support of these incredible mentors and the InsurTech communities, we were able to reach the right decision makers, significantly lower the initial trust barrier and gain a deep understanding of the long-term journey we are on together with our partners.

From the insurer’s point of view, what are the first three questions they should ask themselves before introducing satellite-based risk analyses?

From the insurer’s point of view, it must rigorously clarify three critical questions before introducing satellite-based risk analyses:

1. How exactly does the company’s risk model work and what is its specific competitive advantage? This goes beyond marketing jargon; the insurer needs to understand the proprietary methodology and unique value proposition.
2. What are the validated statistical metrics for precision and recall, and which ground truth dataset is used to validate this information? Transparency regarding precision, false positives and the quality of the real data used for validation is essential.
3. Is the solution scalable at a national level or limited to a small area, and what is the cost structure? The insurer must ensure that the provider is able to efficiently process and deliver data across its entire portfolio without incurring prohibitive scaling costs.

Where do you see Caelus.space in five years’ time? What milestones would signal success in both the insurance and infrastructure markets?

We expect to be an integral part of the European risk monitoring landscape in five years’ time. We expect our customer base to grow significantly in the infrastructure sector, particularly in the monitoring of major projects such as roads and railroads. In the insurance world, we will be an integral part of NetCat departments and a key tool for risk engineers.

Our ultimate success will be to be recognized as a key partner that helps insurers proactively reduce systemic risk, making the insurance company a true partner that fixes and prevents problems, not just compensates in times of crisis.

The questions were asked by Binci Heeb.

Amit Helman, Founder and CEO of Caelus.Space, a remote sensing and geotechnical engineering expert with 15 years of experience in the field. He is also a university lecturer in these domains, holding first and second degrees in Geotechnical Engineering, and is currently a PhD candidate in Geoinformatics. He is married, and in his free time he enjoys off-road jeep trips, trekking, and a good smoky whisky.

Read also: InsurTech as a Bridge Builder: Kobi Bendelak on Israel, Switzerland and New Hubs

European travel insurance company ERV is increasingly relying on artificial intelligence to speed up claims processes and improve the customer experience. CEO Walter Wattinger talks about the balance between automation and human judgment, trust in algorithms and the limits of efficiency.

Walter Wattinger, CEO of Europäische Reiseversicherung ERV, on the status, benefits and next steps.

Mr. Wattinger, how far along is ERV in automating claims with AI and why are you starting with pet insurance of all things?

We have already automated a number of steps in claims processing. With “Pet Excellence”, we are now taking our pet insurance wau-miau to a new level end-to-end: from receipt of the veterinary document to payment. There are two reasons for the focus on pet insurance: strong growth and a high claims frequency per policy. Both require efficient, scalable processes.

Which problem do you solve most internally?

We deliberately rely on the specialist knowledge of experienced veterinary practice assistants when handling animal claims. This expertise is rare and in high demand. With AI, we relieve the team where routines dominate and ensure quality for complex cases where experience counts.

In which phases of the claims process is AI used?

Veterinary invoices are heterogeneous, handwritten, digital and structured differently. Our system reads them item by item, compares them with product and package rules and visualizes everything in a UI for the employees: Original invoice, extracted items, service module, cover yes/no. The AI therefore makes a preliminary assessment, but the decision is always made by the human. A few clicks later, the payment is triggered.

How does this change processing times and the customer experience?

We involved our colleagues right from the start. They helped test UX, curated training data and validated use cases. This increases acceptance and quality. Job profiles are shifting, there is less copy-paste and more analytical plausibility checks at a faster pace. Personal contact with customers and vets is and remains important, so communication skills are also becoming more important.

What do customers notice?

Faster processing and more transparency. Can you see which items are covered by your insurance and which are not? Repetitive steps run in the background, the team concentrates on the challenging cases. The claim is the “moment of truth” for an insurance company. This is where we want to maintain our efficiency, even as the number of claims increases.

How do you ensure traceability and fairness?

Firstly: human-in-the-loop. Secondly, a clean audit trail that shows what data was available and how it was assessed. Thirdly: consistent application of the General Terms and Conditions of Insurance and internal directives. In this way, identical cases lead to identical results. This is all part of our multi-layered guardrailing (drawing up guardrails to prevent misconduct or undesirable results).

What database does reliable AI need in this setting?

Good training and work data, in the relevant languages, complete, legible, interpretable. “Garbage in, garbage out” (if you feed bad, incorrect, distorted or incomplete data into a system, you will get bad results) applies here without compromise. We continuously verify, validate and further develop the AI methodology with ongoing cases.

And the acceptance of automated decisions?

Transparency is the key. We openly characterize the use of AI. For example, when uploading the invoice. Where questions arise, we are prepared and available. The decisive factor always remains: in the end, a human being makes the decision.

Regulation & data protection: How do you stay legally compliant?

We work in a highly regulated environment. Internal group directives on the use of AI, legal reviews, suitable system environments for external development partners and regular internal audits ensure that data protection and compliance are adhered to. Trust and insurance are inextricably linked.

Where do you draw the line for AI decisions?

Today, it is always the human being who decides. In the future, we will be discussing borderline cases. One thing is clear: regulation, such as the EU AI Act, sets the framework within which we design responsibly.

What role do start-ups and technology partners play?

They are important sources of inspiration. In practice, the wheat is separated from the chaff as soon as it comes to integration into core and peripheral systems, APIs and data protection requirements. We also want to expand our collaboration with universities, as science and business are mutually beneficial.

Where is claims processing heading: generative AI and end-to-end?

With “Pet Excellence”, the entry point is first for repetitive tasks, next we look at disease histories for more efficient assessments across multiple data points. This can be scaled to other lines of business. In parallel, we are driving fraud detection to identify abuse earlier and more accurately.

How do you measure success in the near future?

On hard KPIs such as the average claims settlement time. We also monitor the reconsideration rate, which is a good reflection of effort and quality, and listen carefully to how job satisfaction develops within the team. It is important to me that efficiency should never come at the expense of fairness and quality.

What are the next AI priorities outside of claims processing?

In customer service with intelligent triage of incoming inquiries and high-quality, pre-formulated initial responses, but always with a human assessment. We also see potential in pricing, where large volumes of data and finer pricing create clear added value. And, of course, the scaling of claims automation to other lines where it makes sense.

AI accelerates and structures, but it does not replace the judgment of experienced people. It is precisely this interaction that makes the difference.

The questions were asked by Binci Heeb.

Read also: ERV presents new wau-miau pet insurance package: more security for your pets thanks to telemedicine

Builtstop, a Swiss engineering startup, has just won the Swiss Sustainability Challenge 2025 for its groundbreaking innovation NegSV. It is a smart seismic energy absorber that can be installed on the top floor of existing buildings without invasive construction work. The company was founded by a multidisciplinary team of civil engineers and researchers from ETH Zurich and leading European institutions and makes earthquake safety a matter of sustainability: protecting human lives, avoiding construction waste and extending the lifespan of buildings.

With planned pilot installations in southern Europe and long-term ambitions in Japan, Builtstop aims to redefine how cities can prepare for earthquake risks while reducing their carbon footprint. Thebrokernews speaks with Konstantinos Chondrogiannis.

In simple terms, how does NegSV work and how does it differ fundamentally from conventional earthquake protection measures?

NegSV acts as an energy arrester for buildings. It is installed on the top floor and absorbs and diverts earthquake energy away from critical building components. Unlike conventional retrofits, no heavy materials or foundation work is required. The system is non-invasive, lightweight, faster to install and far less expensive, making large-scale earthquake protection practical and sustainable.

What scientific breakthroughs or research results have made this technology possible?

NegSV builds on advances in metamaterial mechanics and negative stiffness systems. Research at ETH Zurich has shown that controlled negative stiffness can efficiently dissipate vibration energy without adding mass, making lightweight, modular earthquake protection possible for the first time.

Is NegSV suitable for both old and new buildings or is it more suited to a specific typology?

NegSV was developed primarily for existing buildings, particularly those built before the introduction of modern seismic regulations, and offers a practical and cost-effective way to improve safety without structural alterations. However, it can also be integrated into new buildings to optimize their design. By incorporating NegSV at the design stage, engineers can safely reduce the dimensions of certain building components to achieve the same or better seismic performance at a lower overall construction cost. This makes NegSV valuable for both retrofit and forward-looking, cost-effective design.

How can disruption to tenants and companies be avoided during installation?

NegSV is only installed on the top floor, leaving the lower floors and the majority of day-to-day operations unaffected. The installation process is quick and non-invasive and is usually completed within a few days. Only tenants on the top floor may need to temporarily relocate for a very short period of time to ensure safety during installation. Once installed, the system requires no structural demolition work or heavy equipment, so buildings can remain largely occupied, functional and undisturbed throughout the process.

You position seismic safety as a sustainability measure. Why do you think that the resilience of cities has so far been neglected in the sustainability debate?

Sustainability often focuses on energy and emissions and overlooks resilience to disasters. Yet earthquakes cause enormous environmental and economic damage. By preventing damage and avoiding reconstruction, seismic resilience is one of the most effective forms of sustainability.

How much CO₂ can cities save by retrofitting instead of building new ones?

Retrofitting with NegSV can save up to 300 percent of CO₂ emissions per building, as demolition and reconstruction are avoided. Extrapolated to the entire city, this means a saving of thousands of tons of carbon, which is directly in line with the goals of climate protection and the circular economy.

Do you see potential for NegSV to become part of building regulations or ESG frameworks?

Yes, NegSV is in line with ESG and EU resilience goals and combines safety and sustainability. As the regulatory framework evolves to include resilience metrics, NegSV can help cities and investors meet both structural and environmental compliance standards.

Why are your first pilot projects planned for Southern Europe and what criteria were used to select them?

Southern Europe has some of the oldest and most vulnerable building stock in earthquake zones, coupled with increasing regulatory pressure to retrofit. These regions offer ideal conditions to validate the technology and demonstrate its social and economic impact on a large scale.

Japan is an important market for seismic innovations. What are your long-term expectations there?

Japan is the global benchmark for seismic technology. Our aim is to enter this market through strategic research and industrial partnerships and offer a complementary solution to existing high-end systems that is lighter, more cost-effective and easier to retrofit in densely built-up urban areas.

How do you convince local authorities and building owners who assume that earthquake protection is too expensive or too disruptive?

We show you data. NegSV is 80 percent cheaper and 90 percent faster to install than conventional methods. No heavy construction work and minimal downtime are the key benefits of our technology. The building remains operational and security is significantly improved. That’s a clear win in terms of cost, time and community resilience.

What regulatory challenges have you encountered so far?

Compliance with local seismic regulations and certification standards is required, but there are no structural or legal barriers. Full validation by ETH Zurich ensures a smooth path to certification and compatibility with Eurocode 8 and national guidelines.

Who do you see as your biggest competitors? Traditional retrofits, new technologies or the inertia of the market?

Our main competitor is still market inertia. The widespread belief that seismic retrofits are too complex or too costly. Traditional methods such as base isolation or shear wall strengthening are well validated and effective, but they are also highly invasive, time consuming and add significant weight to existing structures, often making them impractical for widespread application. New technologies show potential but rarely achieve full validation. Builtstop bridges this gap, offering scientifically proven performance with a lightweight, fast and non-invasive solution.

What happens when the next big earthquake hits a city where NegSV is used? Can you quantify the expected damage reduction?

In simulations and full-scale experiments, NegSV reduces acceleration and displacement between floors by up to 50 percent, significantly lowering the risk of structural damage or collapse. This results in fewer injuries, lower repair costs and faster recovery for entire communities. In addition, in densely populated cities where buildings are very close together, NegSV helps prevent collisions between buildings, an often overlooked but serious cause of earthquake damage in urban environments.

Your founding team combines academic research with practical engineering work. How has this multidisciplinary approach shaped Builtstop’s culture and progress?

This is our greatest strength. We combine academic precision with practical pragmatism. Our culture is based on experimentation and problem solving, which allows us to translate cutting-edge research into practical, scalable products that truly improve the resilience of cities.

What is the most common misconception that engineers or policy makers have about earthquake protection today?

That earthquake safety must be associated with high costs, disruptions and downtimes. NegSV proves the opposite. Earthquake protection can be affordable, fast and sustainable, making resilience an achievable standard rather than an expensive luxury.

The questions were asked by Binci Heeb.

Read also: Courage for the future – the winners of the Swiss Sustainability Challenge 2025

The way insurers communicate with their brokers is no longer fit for purpose. Millennials and Gen Z no longer want long meetings or static brochures. They expect concise, visual, and personalized information at their fingertips. Yet most carriers still rely on outdated tools that fail to engage. 

For Chetan Saiya, founder and CEO of Zoomifier, this “broken infrastructure” between carriers and brokers represents both a challenge and a massive opportunity. His video-driven sales enablement platform promises to turn static content into dynamic, personalized experiences, helping insurers reconnect with their distribution partners and, ultimately, with a new generation of customers.

Chetan, you built Zoomifier from scratch. What was the original vision, and how did you move from media and entertainment, and sports into insurance?

Around the turn of the century, as the internet and digital age fully dawned on us, businesses took an easy shortcut with content that always bugged me. They simply converted paper based sales material and transparencies into PDFs and PowerPoints. While they eliminated the physical medium (paper and foils) , neither of them fully leveraged the interactive rich media and tracking powers of the digital medium to better engage with their customers. So after my previous venture, Assetlink was acquired by SAS Inc., I started Zoomifier to help businesses enable and coach their sales teams to better engage their customers. This was the original vision. At Zoomifier we enable  businesses to easily create, manage, personalize, distribute and track engagement with video-rich content. 

As is required for any startup venture, we first focused on the early adopters for whom video and other rich media were essential to communicate the experience of their offerings. This included industries such media & entertainment (for ad sales), sports (for selling sponsorships), hospitality (for selling event spaces) and real estate (for selling and leasing properties). Insurance was nowhere on the radar for us.

Post COVID, we had acquired a few insurance carriers as customers without any proactive or focused outreach. Initially, we didn’t pay much attention but at the end of the second year we noticed something that blew our minds away. In their first year, they were using Zoomifier to enable their sales teams with traditional PDFs and PowerPoints while they were working on transforming their content to more engaging and interactive content. Once that transformation was complete, in the second year they were creating six times more video-rich content, sharing it with eight times more contacts and seeing a 300% increase in customer engagement compared to the previous year! We had never seen such a rapid performance improvement across multiple customers in any industry. This is when we decided to seriously focus on the insurance industry.

What did you learn from talking to 45 brokers about their daily challenges?

The first thing we did was to reach out to our insurance customers and ask them their reason for adopting Zoomifier. One of them described the challenges they faced in their relationship with broker partners and how Zoomifier was helping them overcome these challenges. Another insurance carrier customer went a step further. In addition to describing the challenges they faced with their distribution partners, they invited us to talk to them so that we can get better insights into their problem. We, of course, jumped at that opportunity. 

In talking with both the brokers and agents as well as the sales, marketing and distribution and partnerships teams at insurance carriers we learnt the following:

1. The brokers did not find the sales content supplied by the insurance carriers to be very useful in helping them close the deals. Most of the information was complex, long, and text heavy. It was not easy to simplify and personalize this information to address buyer’s requirements. So they made up their own content for the buyers!
2. The rapidly emerging Millennial and Gen Z buyers prefer receiving relevant information over meetings. However, they also have a very low attention span and hate reading. The broker channel was struggling to engage this new generation of buyers using traditional sales tools and content. 

Overall, we learnt that brokers gravitate towards promoting offerings of the insurance carriers that make it easy for them to sell faster.

You said the infrastructure between carriers and brokers is “broken.” What exactly isn’t working?

1. There is no easy way for brokers to find the most relevant and up to date content  from the carriers or have easy access to coaching that can help them position the offerings appropriately.
2. It is not possible to easily personalize the information supplied by the carriers so that it can relate to the buyer’s requirements.
3. The carriers have no insights into how their content is used by the brokers to engage customers.

How do generational changes, especially the habits of Millennials and Gen Z, reshape broker engagement?

1. The new generation of buyers prefer receiving relevant information over meeting with agents
2. They hate reading text heavy documents and prefer to watch videos
3. They have a very low attention span and get easily distracted

How does Zoomifier transform the way carriers and brokers communicate?

Zoomifier has introduced 5 innovations that positively impact the carrier – broker relationship:

1. Zoomifier’s personalized engagement Spaces make it extremely easy for brokers to access the most relevant product information and coaching from the carriers.
2. Zoomifier’s Narratives transform existing PDF based product information into highly personalized, bite-sized videos that are easier to understand and more relevant.
3. Zoomifier CustomShow transforms existing text heavy PDFs and PowerPoints into video-rich interactive content libraries that can be easily personalized by the brokers without breaking regulatory or brand compliance.
4. Zoomifier Digital Library makes it easy for insurance carriers to manage and distribute unlimited amounts of video-rich content to brokers with complete access control, password protection, automated content expiry and version management. Zoomifier eliminates the need for downloading any content. All information is manipulated, shared and viewed within Zoomifier environment
5. Zoomifier Analytics tracks how, where and when every content item is used and shared by the brokers as well as how buyers engage with this content. This powerful insight enables brokers to clearly understand buyer’s purchasing intent as well as enables the carriers to determine how well their content is performing.

What does a “personalized engagement space” look like in practice?

Traditionally, the availability of most of the product information is communicated from the carriers to the brokers via email. As a result, the relevant information is scattered across a broker’s inbox. Brokers often download this information on their desktops or network drive. This breaks the connection and the information gets quickly outdated.

In contrast, Zoomifier’s Space is accessible as a single link that is accessible across the whole brokerage or personalized for each broker. The broker simply clicks on the link and all of the relevant information is accessible. Each link is protected through multi-factor authentication so only the relevant members can access the information. Furthermore, the brokers can easily share information with their clients with links to individual content items. The content is always guaranteed to be up to date.

All of the interactions by the brokers and engagement by the customers are completely tracked and available as analytical insights. 

Here is a screenshot of the one such Space:

You mention transforming PDFs into highly interactive, video-based experiences. Can you give an example of how that changes the sales process?

Here are two very commonly used examples:

1. Coaching: Instead of requiring broker agents to attend classroom sessions or webinars, insurance carriers can now create bite-sized videos on relevant topics that agents can reference as and when required (ondemand training). Distributing such training through Zoomifier enables the insurance carriers to understand how well the agents are coached on their product offerings.
2. Personalized Client Engagement: In response to a client inquiry, instead of sending text heavy and long PDFs, the insurance carriers or brokers can create 2-3 minutes personalized videos that quickly walk through relevant parts of the PDF highlighting how those features meet client’s needs. The video also shows them talking to the client. This is almost like meeting in person and explaining to the client. 

Insurance carriers have identified dozens of similar use cases that improve their client engagement and accelerate the sales process. These use cases are applicable across Life & Annuities, Property & Casualty, Health & Benefits and more.

Why should carriers, rather than brokers, finance this engagement technology?

There are several reasons:

1. At the end of the day, the clients are buying insurance carrier’s products. So it is in the interest of the insurance carriers to learn how the broker partners as well as their customers are engaging with their content.
2. Carriers have the processes and resources to adopt, deploy and manage this infrastructure. 
3. Carriers are the ones creating all the content and hence they need a way to completely control access and manage versions.

A few large insurance brokerages have purchased Zoomifier on their own but there are thousands of smaller agencies who do not have adequate resources to manage it themselves. 

What kind of analytics can carriers gain from these interactions, and how can that data drive smarter marketing or training decisions?

Zoomifier helps carriers and their distribution and partnership executives gain real-time, multi-dimensional insights:

1. Which are the most active brokers? 
2. What products are they promoting the most?
3. What content do buyers engage with the most to make their buying decisions? What content is helping with customer retention? What content is effectively promoting best practices that minimize claims?

Where are the customers engaging with this content? At what time of the day? On what devices? 

Equipped with this information marketing managers are able to fine tune their content to improve customer engagement. The analytics help distribution and partnership managers identify the brokers who may require additional coaching on specific products.

You’ve talked about using AI for personalization and content generation. What role do you see for AI in the next stage of sales enablement?

While experimenting with AI in their IT labs, most insurance companies are wary of the dangers posed to personally identifiable information (PII) as well as hallucinations caused by AI that could misrepresent carriers and cause regulatory compliance issues. In this context, sales enablement provides a very low risk approach to try out AI in the field and measure the efficiency gains. 

Here are a few examples of where generative AI is very helpful in sales enablement:

1. Generative AI and associated technologies can automatically convert text heavy PDFs into bite-sized coaching videos for the brokers and product explainers for the customers.
2. AI and semantic searching helps automate personalization of product information based on the client’s insurance needs. 
3. AI can automatically tag and index content so that the users can make intuitive, natural language requests to find the relevant content.
4. AI can interpret analytical insights to coach brokers on how to best engage customers.

None of these examples require use of any PII data. All of the generated information can be verified by the brokers before sharing them with the users.

How can tools like Zoomifier reduce the need for coaching and training webinars while keeping salespeople effective?

Most coaching and training webinars are ineffective because the attendees are multi-tasking during the webinars and barely retain any information after a few days. In contrast, Zoomifier based bite-sized coaching and training content easily accessible to the salespeople enables them to train on demand in response to an inquiry by the customer. Knowing that they have easy access to the relevant coaching improves their confidence in promoting the products for which such coaching is available. 

You mentioned interest in collaborating in the DACH region. What makes this market interesting or challenging?

In the past, I have always found DACH region business managers being highly pragmatic and process oriented. They don’t impulsively jump on to the  technology but instead verify the relevance of the technology and then methodically roll it out. This results in longer term relationships and innovations that actually benefit the users.

This is why I find engaging with business managers in the DACH region interesting and productive.

What’s your message to insurance executives who still see “digital engagement” as just another buzzword?

If you do not grasp the relevance of “digital engagement” in making it easier for your broker agents to promote your brand with the next generation of buyers then:

1. You risk losing the mindshare of your broker partners 
2. Your customer acquisition and retention will be negatively impacted. 

If you could describe the future broker–carrier relationship in one word, what would it be?

“Essential”.

The questions were asked by Binci Heeb.

Chetan Saiya: CEO, Zoomifier Corporation a pioneer in video-rich sales enablement focused on the insurance industry.

He is a serial entrepreneur and founder of successful, self-funded startups that have been acquired by larger software companies. His secret to success is that he believes in deeply understanding the problems faced by an industry and then working relentlessly to innovate a  new category of software to solve them. 

Chetan has MS in Computer Engineering from University of Notre Dame, USA and B.Tech in Electrical Engineering from Indian Institute of Technology, Mumbai, India.

Read also: The future of the insurance industry: a look at five innovative insurtechs

While InsurTechs have long been setting the standards in the industry in many countries, Switzerland is still considered cautious when it comes to the productive use of new technologies. However, there are also some good examples from Switzerland: with artificial intelligence noimos is accelerating claims processing in the motor vehicle sector and making it more efficient, transparent and sustainable. In this interview, CEO Daniel Meier explains why AI should not be a black box, how he sees the industry changing and what Switzerland needs to do to keep up with innovations in the insurance industry.

thebrokernews talks to Daniel Meier, CEO and co-founder of noimos, about the use of AI in claims management and the culture of innovation in the Swiss insurance market.

Mr. Meier, noimos was originally founded as a subsidiary of AXA Switzerland. When did you decide to become an independent company and why was this step necessary?

AXA got to grips with the technological trends surrounding AI at a very early stage and recognized that a great deal of value could potentially be created. At the same time, it quickly became clear that certain developments would require a different environment to that of a large insurance company. This was also with the vision of not only using these services internally at AXA Switzerland in the future, but also opening them up to the market.

What was the vision at the beginning of noimos? Was it primarily about increasing efficiency, or about a fundamentally new understanding of damage recording and assessment?

The vision has remained fundamentally unchanged since the beginning. The aim is to create added value for various players in the field of motor vehicle damage. The digital, AI-supported services offered are designed to meet this need. For example, the garage owner can receive a quick repair approval, while insurers can apply the existing capacities of vehicle experts more specifically to cases where a detailed examination is appropriate. Customers also benefit from efficient claims handling and the potential to avoid unnecessary claims payments, which can have a positive impact on the customer experience and premium levels.

The Swiss insurance market is considered solid, but also rather conservative. How open is the industry really to InsurTech innovations today?

I would not confirm this statement across the board. In the motor vehicle claims environment, I personally feel that insurers are very open to breaking new ground and using new technologies. However, this may also be due to the fact that AXA, as the largest motor insurer in Switzerland, co-develops and also uses our solutions, which is seen in many places as a sign of quality.

Your company relies on computer vision, i.e. the visual analysis of vehicle damage. How does this work in practice and what distinguishes noimos from other AI providers?

We currently have three main products. The first is about increasing the repair rate for windshield damage. The key capabilities in this process are computer vision models that can assess whether the windshield can be repaired based on a photo.

The second product is a “mini vehicle expert” which, like a human vehicle expert, compares the damage pattern with the garage’s cost estimate in order to assess whether the proposed repair work is correct or whether intervention is required. For example, if the damage pattern indicates that dents will also be repaired that are probably from an earlier damage event. Or must be from a collision. Or cannot be parking damage from the insurer’s point of view. These are just a few examples.

The technical capabilities behind these products can be combined and used in very different ways.

Due to our roots, we have a very sound understanding of insurance. Accordingly, we develop our products from the perspective and with the know-how of an insurer who wants to use technology for insurance processes. It sounds simple, but that’s what sets us apart from others.

You attach great importance to ensuring that your AI remains comprehensible and is based on expert knowledge. Why is transparency particularly important when it comes to technological innovations in insurance?

Transparency is a legal requirement and, together with trust, forms the foundation of every insurance company. Our services must therefore be just as transparent and trustworthy.

How is your AI trained and how do you ensure that it works reliably, objectively and across markets?

I wouldn’t talk about “the AI”. Our products consist of different components. We develop certain models in-house, while for other models we use current Large Language Models (LLM), which we adapt to our needs or develop further with our data. And our business rules, which also contribute a great deal of insurance expertise, are central to this.

We ensure reliability to the extent that our models are continuously checked for correctness by having random samples revalidated by humans. Automated standardized tests are also carried out before the release of new versions in order to prevent fundamental malfunctions (e.g. biases or discrimination).

However, the issue of “cross-market” is not quite so trivial. The underlying components are certainly the same. But each country still works very differently. From a process perspective, but also with regard to the insurance conditions.

It is therefore important to define together at the outset what the processes should look like in collaboration with our systems in order to generate as much value as possible. This is precisely where we can differentiate ourselves and create added value thanks to our insurance expertise.

You write that you use LLMs. But anyone can do that. What is innovative about that?

As already mentioned, we use various components in the background. Some of these are based on our own models, others on LLMs and others use hybrid approaches. However, the highly scalable productive operation of LLMs in particular places significantly higher demands than one-off use by individual users via ChatGPT. Experiences from such individual cases are not readily transferable. Furthermore, aspects such as compliance and efficient decommissioning of models have not yet been taken into account in this context.

Your systems combine machine intelligence with human expertise. How do you strike a balance between automation and the indispensable experience of experts?

Machines are more efficient than humans in certain tasks, while at the same time there are activities that are better performed by humans. The products are designed to provide support functions where they are needed and to enable efficiency increases through evaluations. At the same time, the aim is to automate processes where automated solutions offer advantages in terms of effectiveness or costs. The aim is to use existing resources efficiently and automate suitable tasks, but not to replace as many human workers as possible.

Noimos will also work with various other insurers and international partners in the future. What were the biggest hurdles on the way from an AXA project to an independent InsurTech company?

Our owner is still AXA Switzerland, so I wouldn’t see us entirely as an “independent InsurTech company”. However, we also see this situation as a huge advantage because an insurer thinks and invests for the long term more than certain other investors would. After all, they ultimately want to create value for insurers.

If you look at the DACH region: What differences do you see between the willingness to innovate in Switzerland, Germany and Austria?

In my view, the willingness to innovate is not dependent on countries. It stands and falls with the mindset of individuals. Quite apart from the fact that there are major differences in the formal hurdles that need to be overcome.

Many insurers want to appear innovative, but shy away from implementation. How do you convince decision-makers to actually integrate AI solutions into their processes?

Ultimately, the decisive factor is the added value a solution offers. Our experience to date shows that new ideas and application examples often emerge quickly after the start of collaboration.

Your technology also contributes to sustainability, for example when a windshield is repaired instead of replaced. Is this aspect appreciated enough in the industry?

Sustainability is an issue for all insurers. And if this can be combined well with other objectives, so much the better. This is welcomed accordingly.

Are you already thinking about areas of application outside the motor vehicle sector, for example in buildings insurance or industry?

Our main focus remains clearly on motor vehicles. We will see whether this remains exclusively for insurance in the future.

You run noimos with your own team and your own culture, detached from the parent company. How does this independence influence your ability to innovate?

We have the advantages of a small company when it comes to quick decisions and flexibility. This certainly makes us faster than a large organization – but of course we comply with everything that an insurer expects of us from a regulatory perspective.

The shortage of specialists also affects the insurance and tech industries. How do you manage to attract and retain specialists for such a complex field as computer vision?

Employees can see the direct impact of their daily work and are constantly in touch with new topics and technologies, which enables continuous development. At the same time, we invest a lot in team activities because I firmly believe that team spirit and cohesion are key to the success of a company. This also has a positive effect on employee loyalty and also on the long-term retention of employees.

If you look five years ahead: What will noimos look like in 2030 and what impact will artificial intelligence have on the insurance industry as a whole by then?

There are already many areas of application within insurers that are supported by artificial intelligence (e.g. pricing, claims operations, etc.). The types and quantity of applications will certainly increase, as many promising pilots are currently taking place in various areas of the value chain.

We are currently integrating our services in several European countries. My aim is therefore for these implementations to be perceived as a success by our customers and to open us up as a basis for further customers and use cases. I want to establish noimos as a relevant player. You can see from this personal vision for noimos that I have my roots in an insurance company. I have always pursued the goal of really taking and delivering the small and medium steps rather than just talking about the big steps.

The questions were asked by Binci Heeb.

Daniel Meier: Insurance specialist with federal FA, financial planner with federal FA, Executive MBA Sales & Marketing.

1998 – 2001: Insurance apprenticeship at Winterthur Insurance AG, 1999: Founded own company for web hosting/web design, 2001 – 2012: Various roles within distribution at AXA Insurance Ltd, 2013 – 2015: Set up and managed Broker Centre Winterthur AXA Insurance Ltd, 2016 – 2021: Head of Mobility Insurance AXA Insurance Ltd, Since2021: Co-founder and CEO Noimos Ltd.

Read also: Positive signals from the Swiss InsurTech Hub

As insurers grapple with rising data volumes and growing customer expectations, automation has become a strategic imperative. Norway-based AI pioneer Simplifai is introducing a new suite of tools designed to make claims handling faster, smarter, and fully compliant. Ahead of the product webinar, CEO Artem Gonchakov explains how artificial intelligence is transforming claims management, and what it takes to balance innovation with trust.

Founded in Oslo in 2017, Simplifai has earned a reputation as one of Europe’s most agile automation companies, helping insurers, banks, and public institutions streamline complex workflows through AI. Its platform combines document understanding, process automation, and compliance management to reduce manual workloads and improve decision quality. With its latest claims-handling tools, including AI-powered claim summariy, large-loss and fraud detection, and enhanced data capabilities. Simplifai positions itself as a catalyst for the insurance industry’s next phase of digital transformation.

Simplifai is introducing several new AI-powered tools for claims professionals. What specific challenges in claims handling inspired this latest update?

In P&C claims, delays rarely come from big decisions but from the “ground game” — triage, chasing documents, reconciling data across emails, PDFs, and spreadsheets. That’s where leakage creeps in and NPS is won or lost. Our new Simplifai Platform release targets those choke points: Large Loss Detection fast-tracks high-severity claims to senior handlers, Fraud Indication surfaces SIU-worthy signals earlier, and AI Assessment reads and synthesizes large, mixed claim files in one pass.

We’ve also enabled secure, direct use of Excel or Google Sheets inside the workflow, so teams can use the tools they already trust without manual copy-paste. These are practical upgrades built for insurance realities and high volumes. We continue to expand our AI Agents’ skills across the entire claim lifecycle — from intake to closure — ensuring they are genuinely useful in daily operations.

You describe the new platform as improving both efficiency and transparency. How do you define transparency in AI-driven claims processing?

Transparency means no black boxes. Every AI Agent recommendation — coverage check, severity signal, or settlement guidance — comes with clear reasons, audit logs, and human-in-the-loop checkpoints. Leaders can see where AI acted, where a handler intervened, and why. That’s how you defend decisions to auditors, supervisors, and customers.

The new Claim Summary tool sounds like a major productivity booster. How does it ensure accuracy and consistency when summarising complex or lengthy claim documents?

We don’t “skim” — we aggregate. The new AI Assessment reads across documents, emails, images, and estimates, then condenses essentials such as policy terms, parties, amounts, and next best steps into a consistent, reviewable synopsis. Adjusters can merge PDFs and images before summarising, ensuring nothing is left out.

There’s a basic Claim Summary that updates every claim automatically and an advanced version for high-volume, high-complexity cases. Some of our customers receive hundreds of documents and thousands of pages per claim — a task that used to take hours or days. Now the AI Agent can process this information at scale, always updating the claim with the latest data.

Large loss and fraud detection are sensitive areas in insurance. How do you train your AI to identify anomalies without false positives or bias?

We anchor on objective signals — inconsistent documents, timing anomalies, and pattern mismatches versus historical data — and route any low-confidence findings to human review. Large Loss Detection prioritizes potential high-severity claims for senior oversight, while Fraud Indication pre-screens documents and emails for SIU cues. Both operate under client-defined thresholds, so the AI never acts independently.

With Agentic AI, insurers can now detect around 80% of potential fraud cases without purchasing specialty software. We don’t position AI Agents as fraud experts, but as the first layer of defense. They are trained through our Industry Hub using both Simplifai’s data and client-specific rules. When those rules change, the AI dynamically adapts.

You’ve mentioned built-in compliance. How does Simplifai address regulatory expectations such as data privacy, auditability, and explainability?

Trust is built from day one. We apply ISO-style controls, SOC 2 certification, GDPR-aligned practices, and strict data residency options. Privacy by design is central — with encryption, least-privilege access, environment isolation, and granular audit logs. We also use AI to enhance compliance, with bias checks during model training and guardrails to prevent unapproved actions.

Because we address regulatory and governance issues early, our clients can accelerate deployment, reduce rework, and achieve faster value realization.

Many insurers still rely on legacy systems. How easy is it to integrate Simplifai’s tools?

Think of Simplifai’s AI Agents as a non-invasive intelligence layer that sits on top of existing systems — claims, policy, DMS, CRM, or payments — with no rip-and-replace. We can integrate via APIs for modern systems or use RPA for legacy ones. That’s why even older infrastructures can benefit from AI modernization.

How do you see the role of the human claims handler evolving as automation takes over more administrative tasks?

Handlers move from administration to advocacy and judgment. Our AI Agents are designed to cover the full claim lifecycle — but not to automate everything. We design human-in-the-loop processes that combine efficiency with oversight. Humans focus on cognitive and customer-facing work while AI handles repetitive tasks. The future is collaboration, not competition.

There’s a fine line between automation and empathy. How does Simplifai ensure that technology enhances, rather than replaces, the human element?

Customer experience improves when speed and clarity rise while empathy remains intact. AI Agents deliver instant acknowledgment, extract details, provide proactive updates, and reduce repetitive requests. They can also simplify policy language and handle multilingual communication.

However, empathy still belongs to people. AI Agents are programmed to escalate low-confidence or sensitive scenarios to humans, ensuring empathy and judgment where it matters most.

The insurance industry is cautious by nature. How do you build trust when introducing AI into core processes?

Caution is healthy — we meet it with transparency and quick wins. Our adoption playbook:

1. Discovery & Value Hypothesis – model impact on key KPIs using our Value Realization framework.
2. Pilot – focus on a narrow use case, like email or FNOL triage, to prove value quickly.
3. Radical transparency – live dashboards show automation rates, accuracy, and human handoffs.
4. Expand in waves – only after a successful pilot.

This co-design approach turns skepticism into sponsorship because results are visible and governance stays intact.

Can you share a success story?

A UK insurer used our AI Agent for vehicle damage assessments, where handlers previously spent hours reviewing engineer reports and invoices. The AI now flags duplicates or anomalies and suggests actions, improving cost control and fraud detection.

In the US, another client automated property claim intake, processing large volumes of contractor emails and attachments. The AI Agent identifies the right claim, classifies over seventy-five document types, converts formats, and merges images into PDFs. This improved data quality, speed, and accuracy.

You’re introducing capabilities like large loss detection and fraud identification. Do you see Simplifai moving toward predictive or prescriptive analytics?

Yes — that’s the next step. Beyond flagging issues, our agents will propose next best actions with rationale and confidence, like escalating complex cases, requesting second estimates, or adjusting reserves. All under human supervision and with full auditability through our “Know Your Agent” framework.

Are there regional differences in how insurers adopt automation and AI?

The priorities are similar but sequencing differs.

• DACH markets emphasize governance and documentation, so we lead with explainability and audit trails.
• UK and Benelux prefer fast pilots with clear scale-up gates.
• US insurers push for ROI and transparency for regulators.

We tailor rollout strategies to each region’s operating reality.

How does Simplifai’s approach differ from big tech automation platforms?

We’re insurance-first and end-to-end. Instead of toolkits that require integration, we deliver ready-to-go AI Agents per claim type — motor, property, travel, bodily injury — operating under client rules with full Know Your Agent transparency. We integrate as a non-invasive layer, keeping risk low and compounding value.

What’s next for Simplifai?

We’re focused on end-to-end Agentic design, allowing insurers to build, configure, and evolve AI Agents across the full claims lifecycle. Key developments include:

• AI Skill Factory: a growing library of modular insurance-specific AI skills.
• Integration Fabric: seamless connectors and APIs for existing systems.
• Human-in-the-Loop architecture: embedded oversight at every step.
• Know-Your-Agent dashboards: analytics to track performance, accuracy, and ROI.

All of these strengthen control, transparency, and adaptability.

Finally, will claims processing ever become fully autonomous?

Not entirely. The next decade will be defined by amplified human teams. AI Agents will handle 80–90% of routine tasks, while humans focus on judgment, negotiation, and empathy.

Three shifts will shape this future:

1. AI co-pilots assisting adjusters with summaries and recommendations.
2. Multi-modal intelligence combining text, images, and video for explainable outputs.
3. Governed autonomy — straight-through decisions in low-risk cases with built-in explainability and rollback options.

Simplifai is investing heavily in this future, expanding its Skills Library and enhancing KYA dashboards so insurers can safely move from concept to daily operational reality.

Read also:

In the face of growing data volumes and rising customer expectations, automation is becoming a strategic necessity for insurers. Simplifai, the Norwegian pioneer in AI automation, is launching a new series of tools designed to make claims processing faster, smarter and fully compliant. Ahead of the product webinar, CEO Artem Gonchakov explains how AI is changing the future of claims management and how innovation and trust can be reconciled.

Founded in Oslo in 2017, Simplifai has established itself as one of the most agile automation companies in Europe. The company supports insurers, banks and public institutions in optimizing complex workflows through AI. Its platform combines document understanding, process automation and compliance management to reduce manual work and improve decision-making. With its new claims processing tools – including AI-powered claims summaries, major loss and fraud detection and advanced data capabilities – Simplifai is positioning itself as a catalyst for the next phase of digital transformation in the insurance sector.

Interview with Artem Gonchakov, CEO of Simplifai.

Simplifai introduces several new AI-powered tools for claims handlers. What specific claims processing challenges have led to this latest update?

In property and accident insurance claims, delays are rarely caused by important decisions, but rather by the “groundwork”: triage, obtaining documents, reconciling data from emails, PDFs and spreadsheets. This is where errors creep in and determine the success or failure of the NPS. Our new Simplifai platform targets these bottlenecks: “Large Loss Detection” quickly routes high-severity claims to experienced claims handlers, “Fraud Indication” alerts early on to suspicious cases that require SIU investigation, and “AI Assessment” reads and synthesizes large, mixed claims files in one pass.

We’ve also enabled the secure, direct use of Excel or Google Sheets within the workflow, so teams can use the tools they already trust without manual copy and paste. These are practical upgrades designed for the realities of the insurance industry and high volumes. We continue to expand the capabilities of our AI agents across the entire claims lifecycle – from intake to close – ensuring they are truly useful in day-to-day operations.

You describe the new platform as an improvement in both efficiency and transparency. How do you define transparency in AI-driven claims processing?

Transparency means no black boxes. Every recommendation made by an AI agent, whether it’s a cover check, severity level or settlement recommendation, is accompanied by clear justifications, audit trails and checkpoints by humans. Managers can see where the AI has taken action, where an agent has intervened and why. This allows you to defend decisions to auditors, regulators and customers.

The new “Claim Summary” tool sounds like an important productivity booster. How does it ensure accuracy and consistency when summarizing complex or extensive claims documents?

We don’t “skim”, we aggregate. The new AI appraisal reads documents, emails, images and estimates, then summarizes key information such as policy terms, parties, amounts and next best steps into a consistent, verifiable summary. Adjusters can merge PDFs and images before summarizing to ensure nothing is left out.

There is a simple claims overview that automatically updates each claim and an advanced version for high-volume, high-complexity cases. Some of our customers receive hundreds of documents and thousands of pages per claim. This task used to take hours or days. Now the AI agent can process this information on a large scale and always update the claim with the latest data.

The detection of large claims and fraud is a sensitive area in the insurance industry. How do you train your AI to identify anomalies without false alarms or bias?

We rely on objective signals, such as inconsistent documents, timing anomalies and pattern deviations compared to historical data, and forward all results for human review with low confidence. Large Claims Detection prioritizes potentially serious claims for review by supervisors, while Fraud Detection pre-screens documents and emails for indications to the SIU. Both work with customer-defined thresholds, so the AI never acts on its own.

With Agentic AI, insurers can now detect around 80 percent of potential fraud cases without having to buy special software. We are not positioning AI agents as fraud experts, but as the first line of defense. They are trained via our Industry Hub, using both Simplifai data and customer-specific rules. As these rules change, the AI adapts dynamically.

You mentioned integrated compliance. How does Simplifai deal with regulatory requirements such as data protection, verifiability and explainability?

Trust is built from day one. We apply ISO-compliant controls, SOC 2 certification, GDPR-compliant practices and strict data residency options. Privacy by design is at the core with encryption, access with minimal permissions, environment isolation and detailed audit logs. We also use AI to improve compliance, with bias checks during model training and safeguards to prevent unauthorized actions.

By addressing regulatory and governance issues early, our clients can accelerate deployment, reduce rework and realize value faster.

Many insurers still rely on legacy systems. How easy is it to integrate Simplifai’s tools?

Think of Simplifai’s AI agents as a non-invasive intelligence layer that sits on top of existing systems, such as claims processing, policies, DMS, CRM or payments, without the need to completely replace them. We can integrate via APIs for modern systems or use RPA for older systems. Therefore, older infrastructures can also benefit from AI modernization.

How do you see the role of the human claims handler when automation is taking over more and more administrative tasks?

Claims handlers are shifting from administration to advocacy and assessment. Our AI agents are designed to cover the entire claims lifecycle, but not automate everything. We design processes that are human-centric and combine efficiency with oversight. Humans focus on cognitive and customer-facing tasks, while AI takes on repetitive tasks. The future lies in collaboration, not competition.

There is a fine line between automation and empathy. How does Simplifai ensure that technology enhances the human element and does not replace it?

The customer experience improves as speed and clarity increase while empathy is maintained. AI agents deliver instant confirmations, extract details, provide proactive updates and reduce repetitive requests. They can also simplify the language of policies and handle multilingual communication.

However, empathy remains the preserve of humans. AI agents are programmed to forward scenarios with low reliability or sensitive content to humans to ensure empathy and judgment where it matters most.

The insurance industry is cautious by nature. How do you create trust when introducing AI into core processes?

Caution is healthy and we meet it with transparency and quick successes. Our guide to the introduction:

1. Discovery and Value Hypothesis – Model the impact on key KPIs using our Value Realization Framework.
2. Pilot – Focus on a narrow use case, such as email or FNOL triage, to quickly prove value.
3. Radical transparency – live dashboards show automation rates, accuracy and handovers to humans.
4. Expand in waves – only after a successful pilot project.

This co-design approach turns skepticism into support, as the results are visible and governance remains intact.

Can you tell us a success story?

A UK insurer used our AI agent to assess vehicle claims, where previously claims handlers spent hours reviewing engineering reports and invoices. The AI now flags duplicates or anomalies and suggests actions, improving cost control and fraud detection.

In the US, another customer has automated the processing of property damage reports and now processes large volumes of emails and attachments from contractors. The AI agent identifies the correct claims, classifies over 75 document types, converts formats and merges images into PDF files. This has improved data quality, speed and accuracy.

You are introducing features such as large loss detection and fraud identification. Do you see Simplifai moving towards predictive or prescriptive analytics?

Yes, that’s the next step. Beyond flagging issues, our agents will suggest next best actions with justification and confidence, such as escalating complex cases, requesting second estimates or adjusting reserves. All of this is done under human supervision and with full auditability through our “Know Your Agent” framework.

Are there regional differences in the introduction of automation and AI by insurers?

The priorities are similar, but the order is different.

• The DACH markets attach great importance to governance and documentation, which is why we focus on explainability and audit trails.
• The UK and the Benelux countries prefer quick pilot projects with clear scaling options.
• US insurers push for ROI and transparency for regulators.

We adapt the implementation strategies to the operational conditions of the respective region.

How does Simplifai’s approach differ from the automation platforms of the big technology companies?

We are insurance-centric and offer end-to-end solutions. Instead of toolkits that need to be integrated, we deliver ready-to-use AI agents for any type of claim, whether motor, property, travel, personal injury, that work according to the client’s rules and provide full transparency on a “know your agent” basis. We integrate as a non-invasive layer, keeping risk low and increasing value.

What’s next for Simplifai?

We focus on end-to-end agentic design that enables insurers to build, configure and evolve AI agents across the entire claims lifecycle. Key developments include:

• AI Skill Factory: a growing library of modular, insurance-specific AI skills.
• Integration Fabric: seamless connectors and APIs for existing systems.
• Human-in-the-loop architecture: embedded monitoring at every step.
• Know-Your-Agent dashboards: Analytics to track performance, accuracy and ROI.

All this strengthens control, transparency and adaptability.

Will claims processing ever become fully autonomous?

Not quite. The next decade will be characterized by more human teams. AI agents will take over 80 to 90 percent of routine tasks, while humans will focus on judgment, negotiation and empathy.

Three changes will shape this future:

1. AI co-pilots that support experts with summaries and recommendations.
2. Multimodal intelligence that combines text, images and videos for explainable results.
3. Controlled autonomy – direct decisions in low-risk cases with integrated explanation and rollback options.

Simplifai is investing heavily in this future, expanding its skills library and improving the KYA dashboards so that insurers can move confidently from concept to day-to-day operational reality.

The interview was conducted by Binci Heeb, Editor-in-Chief.

With 15 years of experience in insurance, banking, financial services, telecommunications and media, Artem Gonchakov brings a unique combination of technical, operational and business expertise to Simplifai as CEO and board member. With a Master’s degree in Computer Science, he has successfully driven innovation in Fortune 500 companies and start-ups including: Deutsche Bank, Twitter/X, WorkFusion and his own startup Arty Finch.

Read also: AI is becoming a partner to humans – not just a tool

Prof. Dr. Kathrin Kind is optimistic but realistic about the future of artificial intelligence. In this interview, the expert talks about ground-breaking developments in medicine and science, the shift from “human-in-the-loop” to “human-on-the-loop”, the need for global governance and why trust is the decisive currency in the age of AI.

Prof. Dr. Kathrin Kind is Chief Data Scientist and AI Director Global Growth Markets at Cognizant and, among other things, Responsible AI Governor for Switzerland at the Global Council for Responsible AI and Member, Global Future Council on Data Frontiers at the World Economic Forum and as such one of the world’s most recognized experts to talk about the future of AI.

Where do you see artificial intelligence making the most profound impact in the next 10 years in science, business, or society?

I believe the most profound impact will arise from the synergy between scientific discovery and its societal application, particularly in medicine and materials science. In healthcare, we are moving beyond simply using AI for diagnostics. We’re on the cusp of AI-driven drug discovery, where new therapeutics are designed in silico at a fraction of the traditional time and cost. This will fundamentally alter our approach to diseases like cancer and Alzheimer’s. Simultaneously, in materials science, AI will discover novel materials with properties we can barely imagine today, which will be essential for sustainable energy solutions and next-generation electronics. The business world will then rapidly translate these breakthroughs into tangible products, creating a virtuous cycle of innovation.

Will AI remain a supportive tool, or do you expect it to become a true collaborator in research and decision-making?

We are decisively moving beyond the ‘supportive tool’ paradigm. A tool is passive; a calculator, for instance, waits for instruction. AI is becoming a true collaborator. In research, an AI can now not only analyse data but also formulate novel hypotheses and even design the experiments to test them. This is a qualitative shift. The relationship is becoming one of partnership, where the human researcher sets the strategic direction, curiosity, and ethical boundaries, while the AI collaborator explores vast, complex solution spaces, revealing patterns and possibilities that would elude human cognition. We are moving from a ‘human-in-the-loop’ to a ‘human-on-the-loop’ model.

How can we ensure that AI systems are not only technically reliable but also trusted by the public?

Trust is the currency of AI adoption. It rests on two pillars: technical robustness and social legitimacy.

• Technical Robustness: We must advance the field of Explainable AI (XAI). A decision from an AI system, especially in a high-stakes field like medicine or law, cannot be a “black box.” We need systems that can articulate the rationale behind their outputs in a human-understandable way. Rigorous, adversarial testing must also become standard practice to ensure systems are safe and reliable under real-world conditions.
• Social Legitimacy: This is achieved through transparency, clear lines of accountability, and public engagement. People must understand how these systems are governed and have clear avenues for redress when things go wrong. Crucially, ethicists, social scientists, and domain experts must be involved from the very beginning of the design process, not as an afterthought.

Do you believe we need global AI governance frameworks which are similar to climate accords or is national regulation sufficient?

I firmly believe we need global AI governance frameworks. AI is a borderless technology. Its models, data, and effects flow seamlessly across the globe. Disparate national regulations risk creating a fragmented “patchwork,” leading to regulatory arbitrage where companies exploit loopholes in jurisdictions with weaker rules. Much like climate accords or nuclear non-proliferation treaties, we need a global consensus on fundamental principles—safety, fairness, and accountability. This would establish a foundational standard, allowing individual nations to build upon it with more specific regulations tailored to their own cultural and legal contexts.

Despite technical advances, bias in AI remains a challenge. What promising approaches do you see to mitigate this problem?

Bias is one of the most stubborn challenges, as it often reflects and amplifies existing societal inequalities present in the data. There are several promising approaches to mitigation:

• Data-Centric AI: A significant focus is now on meticulously curating and augmenting training datasets to ensure they are balanced and representative. This includes sophisticated techniques for generating synthetic data to fill in gaps for underrepresented groups.
• Algorithmic Fairness: We are developing algorithms with mathematically defined fairness constraints built directly into their optimisation process. This forces the model to balance accuracy with equity metrics, such as ensuring that its error rates are comparable across different demographic groups.
• Continuous Auditing: Recognising that bias is not a problem to be “solved” once, but a risk to be managed continuously. This involves deploying independent, diverse teams to audit AI systems throughout their lifecycle to detect and correct emergent biases.

How do you think AI will change the way we conduct and publish scientific research in the future?

AI is poised to fundamentally reshape the scientific method itself. The process of hypothesis, experimentation, and discovery will be dramatically accelerated. AI will empower researchers to analyse datasets of immense scale and complexity, generating insights that were previously impossible.

In publishing, the traditional, static journal article may evolve. We could see the rise of “living papers”—dynamic, interactive documents that are continuously updated by AI as new data becomes available. AI will also augment the peer review process, helping to check for statistical validity, reproducibility, and even potential plagiarism, thereby increasing the rigour and speed of scientific dissemination.

How should universities and schools adapt to prepare the next generation for a world where AI is ubiquitous?

Our educational philosophy must undergo a paradigm shift. Rote memorisation of facts, a task at which AI excels, must give way to nurturing uniquely human skills. The focus should be on fostering critical thinking, creativity, complex problem-solving, and emotional intelligence.

Schools and universities must integrate AI literacy across all disciplines, not just in computer science. Every student should graduate with a foundational understanding of how AI works, its ethical implications, and how to collaborate with it effectively. The goal is to prepare the next generation not to compete with AI, but to leverage it as a powerful tool for thought and creation.

The U.S. and China dominate much of the AI landscape. What unique role can Europe, and perhaps Switzerland play in shaping AI’s future?

While the U.S. and China may lead in terms of sheer scale and investment, Europe, and Switzerland in particular, are uniquely positioned to pioneer a “third way.” This path is defined by a commitment to developing human-centric, trustworthy, and ethical AI. By championing robust regulatory frameworks like the EU’s AI Act, Europe can set a global gold standard for responsible innovation.

Furthermore, leveraging world-class academic institutions (such as ETH Zurich and EPFL) and a strong industrial base in high-value sectors like pharmaceuticals, robotics, and finance, the region can excel in creating specialised, high-quality AI solutions where trust and precision are paramount. Europe’s role is not necessarily to win the race for scale, but to lead the world in responsible and beneficial AI.

Generative AI tools have already transformed creative industries. Do you see risks of over-reliance, or is this the start of a new human–machine creativity?

I view this as the dawn of a new era of human-machine creativity. History offers a useful parallel: the invention of the camera did not end painting. On the contrary, it liberated painters from the need for pure realism, catalysing movements like Impressionism and Cubism.

Similarly, generative AI is a tool that can augment human ingenuity. The risk of over-reliance leading to creative homogeneity is real, but it is not inevitable. The true potential lies in using these tools as a creative partner—a tireless brainstorming assistant that can help artists, musicians, and writers explore and iterate on ideas at an unprecedented speed. It is a multiplier for human creativity, not a substitute.

Training large AI models consumes enormous amounts of energy. How can we reconcile AI innovation with the urgent need for sustainability?

The environmental cost of training large-scale AI models is a serious and legitimate concern. Addressing this requires a multi-pronged approach:

• Algorithmic Efficiency: A great deal of research is focused on creating more efficient algorithms and model architectures—what we often call “Green AI.” Techniques like model pruning, quantisation, and knowledge distillation can drastically reduce computational requirements.
• Hardware Innovation: The development of new, energy-efficient hardware, such as neuromorphic chips that mimic the brain’s structure, will be crucial.
• Sustainable computing: This includes supplying data centers with renewable energy sources and optimizing their physical location and cooling systems.
• A Shift in Mindset: We must challenge the notion that “bigger is always better.” There is a growing movement towards developing smaller, more specialised models that are highly effective for specific tasks without the enormous energy footprint.

What do you see as the most realistic breakthroughs of AI in healthcare, and what obstacles still stand in the way?

In the near future, the most realistic and impactful breakthroughs will be:

• Radiology and Pathology: AI will become the standard of care in analysing medical images (MRIs, CT scans, biopsies). It will detect diseases like cancer earlier and with greater accuracy than the human eye, acting as an indispensable assistant to clinicians.
• Personalised Medicine: By analysing a patient’s genomic data, lifestyle factors, and clinical history, AI will help predict which treatment protocols will be most effective for that specific individual, moving us away from a one-size-fits-all approach.
• Operational Efficiency: AI will optimise hospital operations, from predicting patient admissions to managing surgical schedules, reducing wait times and improving the quality of care.

The primary obstacles are not purely technical. They are data governance (ensuring patient data is private and secure), regulatory approval (creating clear, efficient pathways for validating medical AI), and clinical integration (seamlessly embedding these tools into doctors’ workflows).

On a personal level: what excites you most about the next stage of AI, and what keeps you up at night?

What excites me most is the potential of AI to function as a universal amplifier of human intellect. I am profoundly optimistic about its capacity to help us solve humanity’s most complex and enduring problems—from developing cures for neurodegenerative diseases to designing fusion reactors and understanding the fundamental nature of consciousness. It’s the ultimate tool for scientific discovery.

What keeps me up at night is the asymmetry between the speed of technological development and the pace of our social and ethical adaptation. My principal concern is the misuse of powerful AI systems, whether in autonomous weaponry, pervasive surveillance, or the creation of sophisticated disinformation that could destabilise societies. We are building something incredibly powerful, and ensuring it remains aligned with humanity’s best interests is the single most important challenge of our time. It is a profound responsibility that we must all share.

Conclusion

The next decade of AI will bring changes in research, business and society. Prof. Dr. Kathrin Kind sees this less as a threat than an opportunity: if we succeed in combining technological innovation with responsibility and foresight. “We are building something incredibly powerful,” she says. “The question is whether we can manage to shape it in the best interests of humanity.”

The questions were asked by Binci Heeb.

Read also: Agentic AI, satellites & start-ups: how innovation is changing the world of insurance

Helvetia is setting new standards in the Swiss pension landscape with a digital self-service pension check and the newly developed Financial Fitness Score. In just 90 seconds, users receive an individual overview of their financial security.

In this interview, Jan Kundert, Head of Customer and Market Management and member of the Executive Board of Helvetia Switzerland, explains why this innovation is far more than a technical gimmick and how it could permanently change the pension culture.

Mr. Kundert, in its press release Helvetia speaks of a “revolution in the Swiss pension world”. What exactly is so new about this digital pension check?

Our self-service check offers interested parties a transparent and comprehensive analysis of their personal pension situation based on the data they enter themselves. What is revolutionary is that the data can be entered in a very short time and that the check generates a “financial fitness score” with which you can compare yourself against a reference group. And last but not least, this service is absolutely free.

Our pension check is also omnichannel-capable, i.e. it can be used in self-service via various channels. At the same time, our advisors have the same tool at their disposal and can directly transfer the data entered by the customer to enable fact-based advice.

What triggered the development of such a tool? Customer needs, regulatory developments or technological possibilities?

The idea was developed together with our partner vlot AG from a strong customer perspective. We wanted to provide our BVG policyholders with a comprehensive overview of their pension situation. Customers want to be covered for unforeseeable risks and for their financial situation in old age. It is not so important whether the money comes from the first, second or third pillar. It must be enough to enable you to lead a carefree life. It is important to have an overall view of all three pillars. Until now, however, gaining this overview has been time-consuming, which has prevented many customers from gaining an overview at an early stage.

Our customers can read the data from the second pillar from the customer portal or directly via the Helvetia pension certificate using a QR code. Without having to laboriously search for the data, as we have found that reading a pension certificate is quite difficult for many insured persons. Supplemented by the data from the first and third pillars, users receive a holistic picture of their personal pension provision, which is presented in an understandable way and shows which measures can be used to close gaps. This is how we optimally combine customer needs and modern technology.

How does the Financial Fitness Score actually work? What data is included and how is a single key figure calculated from it?

The new Financial Fitness Score integrated into the pension check allows you to summarize your own financial situation in a single figure and compare yourself with a reference group. The score takes into account the risk and pension analysis, family constellation, age and financial situation. The various dimensions of the score are weighted according to life phase and gap size before they are aggregated to form a figure. Users can also playfully test how their score changes when a risk is hedged or the savings rate is increased.

The QR code on the pension fund statement is an important component. What role does this innovation play and how did you manage to be the first insurance company to read it in a standardized way?

We have developed this together with our partner vlot AG for our pension certificates. The automated data transfer makes it easier to fill in, minimizes possible incorrect entries and saves time. From 2026, a large number of pension funds will also use a QR code on their pension certificates via the bvg-digital association. We will also be able to read this code in future.

Many people find pension provision complex and confusing. How did you design the tool so that it remains easy to understand and accessible?

Our tool can be operated intuitively by the user. To ensure this, numerous usability tests were carried out with customers. We counteract the complexity with clear presentations of personal pension gaps and brief explanations. An advisor is available at all times to refine the self-service analysis and to discuss users’ needs in person.

How do you ensure that sensitive personal data remains protected and that users really retain control over their information?

The pension analysis is subject to Helvetia’s usual data protection standards. Privacy and personal rights as well as the handling of personal data are important to us. We therefore inform users in detail about the underlying standards before the personal analysis is started.

Users can use the score to simulate various scenarios. How much does the score typically change if, for example, the savings rate is increased or risks are hedged?

This depends on the extent to which the risks have already been hedged and how high the target income and thus the savings rate is set. This depends on the user’s life situation, risk appetite, financial situation, willingness to invest and spending behavior. It is not possible to give a blanket answer here.

To what extent can this tool replace traditional consulting – or is it intended as a supplement?

In any case, it makes sense to start with a self-service analysis, deal with the results yourself and then refine and further individualize the analysis in a next step together with a Helvetia advisor. Pension provision is long-term oriented, based on trust and many components have to be included. That’s why I see the digital tool and personal, individual advice as the ideal combination.

You talk about a “contribution to raising awareness” of pension provision. How do you intend to ensure that younger target groups in particular or people without an advisor are reached?

The challenge of pension provision is the time-consuming collection of all data. This “barrier to entry” prevents many people from taking the first step and getting an overview. With the digital pension check and the option to enter data via QR code, we are removing most of these barriers to entry. It is important to carry out a corresponding analysis as early as possible, as early provision is extremely efficient for younger people in particular due to the compound interest effect.

Comparing one’s own financial situation summarized in a figure with a reference group contains a playful element. Especially in the younger target group, benchmarking is an important element in assessing one’s own life situation. With the Financial Fitness Score, we want to provide a measure that the younger target group can also use for orientation.

How do you involve your sales partners and consultants in this new digital offering? Do you see opportunities or areas of tension here?

In addition to the self-service analysis tool, Helvetia advisors already use an expert advisor version from our partner vlot, which allows an even more in-depth analysis. Sales partners, on the other hand, use their own advisory tools. vlot is a SaaS (Software as a Service) solution that is constantly evolving and can also be implemented by other pension providers.

What role did the collaboration with the start-up vlot AG play? Was the external expertise decisive for the development?

The expertise and innovative capacity of an external partner helped us to develop and implement a modern omnichannel strategy. We have implemented the central element of such a data and channel strategy by means of a deep data connection to our CRM. This allows us to benefit even more from the strength of the tool.

The pension check is also explicitly aimed at real estate owners and people aged 50+. Why this segmentation – and how do the use cases differ?

We differentiate between the cases in terms of the weighting of needs. Younger people should deal with pension provision promptly, particularly with regard to risk protection. For property owners, we see a very large affordability gap in old age, which needs to be addressed proactively. Last but not least, people in the 50+ segment should actively consider their pension situation and pension planning.

How do you measure the success of this new tool? Is it primarily about reach, customer loyalty or actual contracts concluded?

We measure success on the basis of the checks carried out, the downloads of the analyses – which are available free of charge – and the resulting new customers, which are certainly delayed.

What are the next steps? Do you foresee further expansion stages or interfaces, for example to pillar 3a or to bank offers?

A direct online conclusion option from the tool would be a conceivable solution. As far as a bank-side offering is concerned, a successful conclusion of the current merger plans with Baloise and its banking services would of course help us. Integration into our MyHelvetia customer portal is also on the roadmap.

And finally, how will this tool change the role of an insurer like Helvetia in the Swiss pension market in the long term?

With its low-threshold offer, this tool is intended to encourage people to tackle the topic of pension provision and deal with it. For many people, we want to help them take the first step, create transparency, better understand their financial situation and improve it on this basis.

Try out the pension check here
For young people: www.helvetia.ch/vorsorge-check
For property owners: www.helvetia.ch/abgesichert
For 50+: www.helvetia.ch/pensions-check

Conclusion

With the digital pension check and the Financial Fitness Score, Helvetia is introducing a low-threshold, innovative tool that provides a transparent overview of the personal pension situation within 90 seconds. The combination of data integration via QR code, real-time score and self-explanatory nature is intended to make pension provision accessible to all sections of the population. Jan Kundert sees this as an important step towards raising awareness, simplifying processes and strengthening the role of the insurer as a digital partner for the future.

The questions were asked by Binci Heeb.

Read also: Helvetia brings a breath of fresh air to the Swiss pension world