Digital Transformation

AI in insurance in 2025 is overhyped and underdelivering.  There. I said it. We are witnessing an AI frenzy in the insurance industry. Vendors are pitching shiny solutions, boards are eagerly asking about AI strategies, and innovation teams are running around setting up pilots.  On paper, it looks like progress. In reality, most of these initiatives deliver little to no measurable value. “Only 1% of companies describe their GenAI rollouts as ‘mature.’” (Source: McKinsey & Company, 2025)  AI – the magic buzzword in insurance AI is not a magic wand that fixes broken foundations.  It is a multiplier. If you layer AI on top of messy workflows, unclear goals, and poor data, you will get exactly what you built—only worse. AI will amplify the dysfunction. It will accelerate chaos. It will turn small inefficiencies into expensive problems. That is why so many AI projects in insurance follow the same storyline: initial excitement, a few nice demos, then a quiet retreat when no real impact is delivered.  Six months later, the board asks, “Where is our AI ROI?” and the answer is a lot of awkward silence. McKinsey estimates that generative AI and other technologies could unlock up to $4.4 trillion in annual productivity gains across industries. For insurance alone, AI could deliver up to $1.1 trillion in annual value. (Source: McKinsey & Company, 2023). However, that is possible only if we scale AI to create impact. Overhyped and underdelivering – what if the problem is not AI? We often fall into the trap of choosing technology first, then figuring out what to do with it.  Teams get excited about GenAI, machine learning, or automation tools, but forget to ask the most important question: What business outcome are we trying to improve? If that answer is fuzzy, the project is already doomed. AI does not define business goals for you. It does not clean your data. It does not fix broken processes. It will not magically convince your teams to adopt it. These are foundations you need to build yourself before AI can deliver any meaningful results. Over the years, I have seen countless projects fail because we try to solve surface-level problems with advanced technology, without addressing the underlying issues. We throw AI at processes that are full of manual workarounds, exceptions, and legacy systems, hoping it will fix everything. It does not. In fact, it often adds more complexity. AI adoption in insurance AI is a tool. A powerful one—but only when used correctly. And it is not just about technology. How you communicate AI within your organisation matters just as much as the technology itself. Improving explainability Explainability is not a technical luxury—it is a business necessity. If your teams do not understand how AI makes decisions, they will not trust it. And if they do not trust it, they will not use it. It is that simple. Insurers need to move beyond the black-box approach and start designing AI systems that are transparent, with clear logic that human experts can audit, challenge, and refine. Explainability does not mean exposing every line of code. It means showing how decisions are made, in language that makes sense to the people who will use them daily. It means allowing human experts to intervene, correct, and improve the model’s outputs. It means positioning AI as a partner, not a competitor. AI in insurance core processes – people first approach One of the biggest reasons AI adoption fails in insurance is fear. Not fear of technology failing, but fear of people being replaced. Claims handlers, underwriters, and operations teams are often told that AI will “augment” their work, but all they hear is “this machine is here to take over your job.”  That fear creates resistance. Subtle at first: people just not using the tools, ignoring recommendations, sticking to manual workarounds. But over time, this quiet resistance kills adoption. Adoption improves when people feel empowered, not threatened. When underwriters see that AI handles repetitive data processing, they can focus on complex risks.  When claims handlers realise that AI helps them triage faster, while they bring the empathy and judgment that AI cannot replicate.  The message is not “AI will replace you.” The message is “AI will take the grunt work off your plate, so you can focus on the decisions that truly need you.” And this shift starts with leadership. Insurers need to lead this conversation, openly and honestly. A communication and upskilling plan is not a side project—it is at the core of any AI initiative that wants to succeed. Show your teams how their roles will evolve. Involve them in designing how AI integrates into their workflows. Support them with the training and tools they need to feel in control of the change, not at the mercy of it. That is how you build adoption. Not by pushing AI onto people, but by pulling them into the journey. The AI Blueprint Over the last few years, I dug deeper into the question – why most AI projects fail to deliver impact at scale?  We know the drill – most companies run in endless circles, from one proof of concept to another. It is no surprise that we speak about AI being both overhyped and underdelivering on impact. The methodology The AI Blueprint finsurtech.ai created is not a workshop, nor an endless assessment. It is a short, focused engagement that helps insurance teams move from AI experimentation to actual execution.  We look at what matters first: defining clear business objectives, mapping processes and user journeys, and assessing whether your organisation is even ready for AI.  Sometimes, we discover that a simple workflow automation or a process redesign can have a greater impact than any AI model could. And that is fine. The goal is not to use AI for the sake of AI. The goal is to solve problems and deliver value. When I say assess readiness, I mean it. Data quality, decision logic, compliance needs, and explainability requirements are not boxes to be

Everyone is talking about AI taking jobs.Fair enough. But what if the most pressing risk is how AI threatens our cybersecurity? While the world debates automation and employment, a far more dangerous threat is unfolding: AI-powered cyberattacks. In today’s digital arms race, AI is more than a tool – it is a weapon, and a potentially devastating one. What used to be handled by human analysts is now being overtaken by intelligent, self-learning systems, capable of launching attacks faster, smarter, and at unprecedented scale. The same technology we built to defend ourselves is now being turned against us. In this article, we dive into the dark side of AI: the rise of automated threats, the vulnerabilities they exploit, and the impact they could have on our economy, our institutions, and our lives. A New Generation of Threats Arguably, AI is speeding things up, but not always for the better. Today’s cyberattacks are more sophisticated, more targeted, and, most alarmingly, more automated. What once took months of reconnaissance and manual effort can now be executed in minutes with the help of generative AI. Attackers are now using AI to craft highly personalised phishing messages, often in multiple languages. By mimicking writing styles and context, these emails appear strikingly real, making them far more likely to succeed. They also use AI to clone executive voices and faces, enabling deepfake scams. A fake video call from a “CEO” can trick employees into approving fraudulent transactions. Polymorphic malware is another weapon – malicious code that constantly changes to avoid detection. Traditional antivirus tools cannot keep up with these shapeshifting AI cybersecurity threats. AI accelerates the discovery of zero-day vulnerabilities—undetected flaws in software. These are like secret doors into a system, wide open for attackers until discovered and patched. Finally, attackers are poisoning AI training data, corrupting the systems meant to protect us. By feeding AI false inputs, they cause it to ignore real threats or flag harmless actions as dangerous. In the financial sector alone, leaders are raising the alarm. A 2025 report by Business Insider revealed that 80% of banking cybersecurity executives feel unprepared for the rise in AI-driven attacks, despite increasing their budgets year over year. Impact Across Industries The impact of these threats is both broad and deep. In financial services, the rise of generative AI has supercharged fraud. Deepfake-powered scams have already led to high-profile losses, and analysts warn that AI-enabled fraud may quadruple global damages by 2027. In critical infrastructure, the convergence of operational technology (OT) and IT systems has opened the door for attackers to use AI in targeting energy grids, transport systems, and even water utilities. Researchers are now urging hybrid human-AI response systems to anticipate these evolving threats. In insurance and risk, AI-generated synthetic identities are posing challenges to underwriting and claims systems. As identity becomes easier to fake, trust becomes harder to verify. Imagine the impact on risk assessment or claims fraud. As an industry, we were struggling to prevent basic fraud. Are we going to keep up with AI-powered fraud? And across all sectors, existing cryptographic systems are facing dual threats: AI-assisted decryption techniques today and quantum computing threats on the horizon. This has accelerated interest in post-quantum cryptography, with institutions moving from research to implementation far sooner than anticipated. The Trust Gap Despite the clear need for AI in cybersecurity, a notable gap remains: the trust gap between cybersecurity leaders and those on the front lines. A recent TechRadar Pro study found that while over half of executives believe AI improves productivity, only 10% of security analysts trust AI systems to work without human oversight. Tool fragmentation, black-box models, and poor explainability continue to limit adoption and scale. Without trust, even the best tools risk becoming shelfware. The Regulatory Catch-Up The rapid advancement of AI has also caught regulators off guard. With few clear frameworks for AI governance, companies find themselves constrained. Many hesitate to deploy full-scale AI detection or prevention models for fear of unintended consequences or future legal exposure. Meanwhile, attackers have no such constraints. This imbalance is unsustainable. To level the playing field, companies, policymakers, and cybersecurity firms must collaborate to develop agile, risk-based frameworks that enable innovation without compromising oversight. What Must Change Cybersecurity in the AI era cannot be solved with more tools alone. What is needed is a shift in mindset and architecture. Human-AI collaboration must become the new norm. AI is not here to replace cybersecurity analysts but to enhance their capabilities. A well-trained analyst using AI can sift through thousands of threat signals in seconds, prioritise real threats, and respond faster than ever before. For example, platforms like IBM QRadar or Microsoft Sentinel already use AI to reduce alert fatigue and surface critical anomalies. However, they still rely on human judgment to validate and act. Second, unified threat intelligence must break through institutional and geographic silos. Today’s attackers do not respect borders—so neither should our defenses. Cross-sector and cross-border collaboration is critical to track how AI-powered campaigns evolve. Initiatives like the Global Forum on Cyber Expertise and EU-wide threat-sharing platforms are steps in this direction, but more real-time, open data exchange is needed to stay ahead. Third, resilient cryptography must move from pilot to production. As AI accelerates and quantum computing inches closer, traditional encryption like RSA and ECC is no longer enough. Post-quantum cryptographic algorithms—once considered overcautious—are quickly becoming urgent. Governments and institutions are already testing NIST-approved standards, but adoption remains uneven and slow. Lastly, governance and transparency must be built into every AI security deployment from day one. Black-box systems are dangerous in high-stakes environments. If analysts and regulators cannot explain how a model works—or why it made a decision—it undermines trust and opens the door to manipulation. Explainability is not just a compliance checkbox—it is a prerequisite for long-term resilience. In short, defending against AI-powered threats will require more than smart tools. It will require smarter strategies, shared accountability, and systems designed for adaptability and trust. The Stakes Are Rising Cybersecurity has always been a race, but the finish line keeps moving. In the age of AI, the speed, scale, and sophistication of attacks are evolving faster than

Operational excellence is the new innovation. Before you jump out of your seat, hear me out. Innovation in insurance often makes headlines when it is loud: new digital products, embedded insurance, usage-based pricing, ecosystem partnerships. And all of that is important.  But while the spotlight shines on the flashy newcomers, the backbone of this industry is still built on traditional products – motor, property, health, and life. The main problem in our industry is that many of those products are still difficult to buy, understand, and even claim. And that is where the real innovation opportunity lies – not in launching the next digital-only gadget but in reimagining what already works for millions of people and making it work better. The Uncomfortable Truth: Most Products Are Still Clunky Let’s be honest: buying a motor policy still feels like paperwork, even when done online. Most customers are lost when they go through an accident, and filing a claim still feels like a negotiation. Customer service? That is more of a ping-pong game, a “pass-the-request” across departments and systems. It is not the product that is broken – it is the experience around it. If we are serious about innovation, we must stop treating these everyday processes as legacy baggage and start treating them as core value drivers. It is always great to create a new, disruptive product, but without strong operational foundations, it will not fly. Parametric with manual payments defeats the purpose of creating it to begin with. Before creating an IoT-based insurance product, make sure you have solid data processing capabilities.  Innovation Is Not Just the New. It Is Also the Better Innovation is not only about launching something different. It is also about making something better. What if motor insurance could be bought in under two minutes using a conversational interface without the burden of scrolling through twelve pages of fine print? What if I only said what I needed, and a modern LLM suggested the best products based on my particular situation? Or take claims, the ultimate moment of truth in insurance. What if a property claim could be assessed and settled within a few minutes, rather than a week of back-and-forth emails and unclear updates?What if customers never had to chase insurers for information, because the process was so seamless that the next step always reached them first? These are not pipe dreams. They are entirely achievable, with the right focus on operational design, digital infrastructure, and customer-centric thinking. Traditional Products Still Run the Business While we love to talk about disruption, the vast majority of written premiums globally still come from traditional product lines: motor, home, health, and life. These products are familiar to everyone. And they carry the kind of long-term trust that embedded offers or parametric experiments are still trying to earn. If we want to future-proof the industry, we should not abandon these pillars. We should strengthen them by making them easier to understand, easier to purchase, more transparent to claim on, and more responsive throughout the customer lifecycle. Think automation and AI Agents, the future of smartly orchestrated workflows. The good news is that modernising the traditional products does not require reinvention. It requires the maturity and discipline to get the basics right. Operational Excellence Is the New Innovation It might not sound sexy, but fixing operations is where the magic happens. Operations are where expectations are met – or not. Where trust is built – or lost.Where cost savings are made – or wasted. We must simplify overly complex processes, orchestrate workflows to reduce inefficiencies, digitise and streamline claims handling. By enabling modern servicing through technologies like AI agents or rule-based automation, insurers can radically improve the customer experience and free up resources to focus on what really matters. You do not have to change the product to change the game.Sometimes, the real innovation is in making things finally work the way they should have all along. A New Era of Innovation Leadership The insurers that will win the next decade are not just the ones that launch new things; they are the ones that deliver operational excellence and innovation on the core business. They will be known not for how futuristic their website looks, but for how easy it is to buy a policy, how painless it is to file a claim, and how consistently they show up for customers when it matters most. They will turn operational excellence into a visible, differentiating strength—not something buried behind the scenes, but something customers feel and value. Because in a market full of promises, execution still matters. The Bottom Line If we want innovation to mean something in insurance, we have to broaden our definition. Creating new products is fancy, but whatever we do has to work. And the smartest move insurers can make today is not always to chase the next big thing. It is to make the existing big things easier, faster, and more human. Sometimes, innovation is not a launch, but something more effective, like automation and a boost to customer experience. And that is exactly what this industry needs more of.

AI Agents are quietly shaping insurance claims and customer service.  It is often said that change in insurance is slow – until it is not. Behind the scenes, smart, visionary companies work on creating the future of insurance services. Welcome to the era of AI agents. Interestingly, we do not speak about fancy customer apps or shiny front-end portals. The change happens in the plumbing, deep in operations, where decisions are made, claims are processed, and real value is won or lost. We are entering the age of AI agents. But not the kind that write (often bad) poetry or generate marketing blurbs. I mean structured, goal-oriented agents that can coordinate tasks, follow logic, and support real business processes – insurance processes. This matters more than we might think. What is an AI Agent? Let`s start by defining what an AI Agent is. No, it is not a chatbot. Not a voice assistant. An AI agent is more like a digital teammate. For example, let`s think of an AI Agent that processes a claim: it gathers documents, triggers validations, updates systems, and even notifies humans when they need to analyse a complex case.. AI is not replacing people. It rather allows them to work on value-added, creative tasks by handling the repeatable, invisible, frustratingly manual work that clogs up most insurance operations today. Imagine having hundreds of these agents working behind the scenes, keeping processes moving while your team focuses on the exceptions, the complex cases, and, most importantly, customer relationships. Why Claims and Customer Service Are the Natural Starting Points for AI Agents Claims are arguably the moment of truth for every customer. This is the whole point of buying insurance – to get help when the unthinkable happens. And yet, our industry has a poor reputation when it comes to servicing customers when they need it the most. Insurers have spent years trying to modernise their claims processes. Most have achieved partial automation, but very few can say the journey is seamless from FNOL to settlement. The real blockers are not just technology, but process fragmentation, manual handovers, and lack of orchestration. Customer service faces the same challenge. Insurers have focused more on cost-cutting and nearshoring than on customer experience. And the consequences are visible – frustrating queues, answers in line with the rules, but far from solving the customer`s problem. Ultimately, it is not about replying faster; it is about resolving issues efficiently, across departments and systems that were never designed to work together. This is where AI agents, powered by orchestration engines, become game-changers. First, you do not need to rebuild your core platforms or endure months of integration headaches. The AI Agents simply slip into the existing chaos and start making sense of it.  They connect siloed systems that were never meant to speak to each other, like digital translators at a dysfunctional family dinner. They can triage incoming requests, route tasks to the right systems or people, and follow up with robotic persistence – no reminders needed, no sticky notes involved.  And unlike human teams stretched across time zones and inboxes, they work 24/7 without breaks, burnout, or Monday morning moods. They are not here to impress; they are here to get the job done quietly, efficiently, and without ever asking for a raise. Beyond the Hype There is no shortage of hype. We have seen demos that look sleek but break in the real world. The insurance industry does not need more surface-level digital tools. It needs infrastructure that works, even when the rules are messy and the systems are old and too expensive to replace. AI agents, when used properly, are not cosmetic. They are back-end enablers. They do not replace judgment; they enhance flow. And when paired with strong orchestration platforms, they can reduce errors, increase consistency, and dramatically cut down handling times. For sure, you might want to use AI Agents for the hype or a better image in front of investors. But the primary objective of automation is getting the work done faster, better, and in a way that lets people focus on what matters. How to start with AI Agents in Claims and Customer Service The beautiful part of AI Agents automation is that you can start without doing a dramatic tech overhaul or a “burn it all down” transformation. Insurers simply need to do two things, both less glamorous than a keynote speech, but far more effective.  First, take a hard, honest look at current processes: where is the friction, the waste, the duplicated effort, the unnecessary handover? No filters, no fluff – just a sober inventory of how things really work (or do not).  Second, explore lightweight orchestration layers that can act like digital glue, sitting quietly between systems and triggering AI agents to handle the repetitive stuff no one enjoys.  This is not moonshot territory. It is practical, focused, and deeply operational – the kind of transformation that does not make headlines, but does make life easier. And yes – it can be done. The Bottom Line AI agents will not revolutionise insurance in one big bang. But they will quietly start transforming how work gets done. They will reduce delays, improve accuracy, and make life better for the people who process claims and serve customers every day. In the long run, that is how change sticks – not with hype, but with flow. If that sounds unsexy, good. The real revolution often is quiet and effective.

For years, payments have been treated as the backstage crew of the insurance world. Invisible. Underfunded. Nobody’s innovation priority. But that quiet corner is now one of the most potent levers insurers can use to transform the customer experience, improve operational efficiency, and unlock new growth. And it is long overdue. Let`s dive deeper into what payment innovators have in store for us in 2025. And never forget – innovation comes often from the edges. Frictionless Payments Are Not a Nice-to-Have When was the last time you received a paper invoice from Netflix? Exactly. Most industries have long moved on from slow, manual, and fragmented payment processes. Meanwhile, in insurance, paper checks, cash and clunky bank transfers are still far too common. But the tools to change this are already here. Thanks to global schemes like Mastercard and Visa, and modern platforms like Adyen and Stripe, insurers finally have access to the kind of payment capabilities others have used for years. And this goes well beyond just moving money faster: This is not innovation for the sake of it. It is about building a system that works — quickly, securely, and at scale. Because in today’s world, payments should not be an afterthought. They should be an advantage. Payments Drive Growth (Yes, Really) Payments are often treated as a backend task — the final step to wrap things up. Necessary, but not strategic. That mindset is outdated. The reality is, payments have become a powerful lever for growth. When done right, they do much more than make customers happy. They create tangible business value. Faster payouts mean less frustration and more loyalty. A smooth claims experience is not just about empathy — it directly impacts retention. On the operational side, the ability to settle instantly with suppliers builds stronger partnerships and helps deliver better service faster. Digital-first payment capabilities are also unlocking entirely new segments. In markets where traditional banking does not reach everyone, flexible payout options open the door to underserved customers — while reducing the cost of service. And then there is the data. Every transaction holds insight: when people engage, how much they spend, what timing works best. Used wisely, that data can shape smarter product design and more precise go-to-market strategies. Even payment flexibility can be a differentiator. Offering options like split or staged payouts gives customers greater control over their finances — a small feature that can have a big impact on satisfaction. Let us say it clearly: payments are no longer the end of the customer journey. They are part of the product itself. When payments are fast, smooth, and intelligent, everything else flows better — from the way your teams operate to the way your customers feel to the way your business grows. It Is Time to Rethink Engagement When was the last time someone in a car accident opened the App Store? Exactly. And yet, many insurers still pour resources into apps that remain untouched — especially when it matters most. Claims are emotional. Messy. Urgent. They are not the moment to ask someone to download an app, reset a password, or wait on hold. But what if, at the point of sale, you gave the customer something simple — a digital wallet card? Nothing flashy. Just a quiet, useful tool that lives on their phone and springs into action when needed most. That card becomes a personal command centre: No detours, no downloads, no delays. Just one simple space that does what it needs to do — when people need it most. This is what modern engagement looks like. Not asking customers to adapt to your systems, but integrating into theirs. Because when claims go smoothly, trust builds. And when trust builds, so does long-term loyalty. Inclusion Demands Local-First Payment Design Not every customer lives in a card-based world. And not every claims journey runs through a bank account. In many parts of the Global South, cards are the exception rather than the rule. What truly matters are the systems people already trust and use — mobile wallets, mobile money networks, direct-to-account transfers, and cash-out options where digital infrastructure is still limited or unreliable. Designing for inclusion means starting with reality. It means recognising how people actually receive and move money, not how we imagine they should. If your payout system does not reflect local habits and constraints, it will not work. Simple as that. And this is not just a story about emerging markets. Expectations are shifting everywhere. From Nairobi to New York, younger generations want the same thing: speed, clarity, and control. No paperwork, no delays. No waiting in line or sitting on hold. Inclusion and convenience are no longer perks. They are the baseline — the minimum customers expect in a modern experience. Better Payments Without Core Replacement Here is the best part: you do not need to tear down your core systems to modernise payments. No massive transformation program or three-year migration. Build on top of what you have. With the right orchestration layer, you can bridge the old and the new — connecting legacy systems with modern infrastructure without disrupting your entire operation. You can capture data from any channel, whether digital or offline, and use it to automate decisions in real-time, factoring in rules, fraud signals, and the specific context of each transaction. Payouts become fast, secure, and traceable — fully digital, and fully aligned with what your customers expect today. And throughout the process, they stay informed. No guessing. No chasing. Modernising payments does not have to mean replacing your core. It just means making your systems work together — and work smarter. Final Thought: Design for Outcomes, Not Applause The best payment tech in insurance is not the one that wins awards.It is the one that quietly improves underwriting performance, accelerates claims, builds customer trust, and gives you tighter control over your business. Everything else is just theatre. Are your payments still just plumbing?Or are they your next power move?