Digital Insurance Tools

Underwriting is about to experience the same disruption payments saw with UPI silent, intelligent, and hyper-personalized. Traditional actuarial models, largely built on age, gender, and medical history, are no longer enough to accurately price risk. The future of underwriting is about 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞, 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐫𝐢𝐬𝐤 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. A McKinsey study estimates that 𝐀𝐈-𝐞𝐧𝐚𝐛𝐥𝐞𝐝 𝐮𝐧𝐝𝐞𝐫𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐜𝐚𝐧 𝐫𝐞𝐝𝐮𝐜𝐞 𝐥𝐨𝐬𝐬 𝐫𝐚𝐭𝐢𝐨𝐬 𝐛𝐲 𝐮𝐩 𝐭𝐨 𝟐𝟎% through more accurate segmentation and predictive modeling. Insurers are already leveraging geolocation, wearable data, and transaction behavior to assess actual lifestyle risk, not just what’s declared on a form. Instead of pricing a policy once at issuance, underwriting will become continuous. Transactional data from IoT, telematics, and payments will enable dynamic risk tiers such as auto premiums recalibrating monthly based on real driving behavior. With explainability frameworks (like XAI), underwriters can ensure AI doesn’t become a black box. This is critical as 𝟖𝟐% 𝐨𝐟 𝐠𝐥𝐨𝐛𝐚𝐥 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐬 𝐞𝐱𝐩𝐞𝐜𝐭 𝐬𝐭𝐫𝐨𝐧𝐠𝐞𝐫 𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐧 𝐢𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞 over the next 3 years The top insurers are building ecosystems. Partnerships with mobility, fintech, and health platforms will give them richer, more reliable signals, transforming underwriting from risk prediction to risk prevention. The underwriting engine will sense, learn, and adapt in real time, turning insurance from reactive protection to proactive resilience. #DigitalIndia #Fintech #AI #technology #Fintech #technology

AI in insurance is not a productivity hack 🚫 Automating the past is safe and will generate marginal returns. The real value lies in underwriting the future! AI is being talked about everywhere in insurance. Too often, the conversation stalls at efficiency theatre. Faster underwriting. Cheaper claims handling. Fewer people doing more work. Useful, but small. The real opportunity sits elsewhere. Reimagining Risk in an AI-Driven World, developed by the International Insurance Society, captures this shift well. Having contributed to the report and led the executive workshop in Zurich, one message came through very clearly: the next decade will separate insurers making marginal improvements from those rebuilding their operating models around new forms of risk, data, and human judgement. AI is not the strategy. It is the unlock 🔓 The strategic upside is not incremental. It sits in: • New insurable risks emerging from intangible assets, cyber, AI, and climate • Proprietary knowledge graphs, data, decision systems become a true edge • Human judgement being augmented, not replaced, in a trust-based industry • Governance, talent, and data strategy becoming board-level differentiators, not IT issues 🤩 One stat should give leaders pause. Nearly 90% of firms are experimenting with GenAI, yet only around a quarter have anything in real production. Plenty of motion. Limited transformation. That gap is not about technology. It is about operating model courage. Keen to hear from peers across insurers, reinsurers, brokers, MGAs, and insurtechs: • Where have you seen AI move the needle beyond efficiency? • What is genuinely blocking scaled deployment? • Are we underwriting new risks fast enough, or just automating old ones? If insurance gets this right, we don’t just adapt to an AI-enabled world. We become one of its core stabilisers. Thoughts and counter-views welcome. Full report link in comments 👇 Anders Malmström, Joshua Landau, Colleen McKenna Tucker

Does Insurance need to stop calling itself “Insurance” to grow in Africa? Yes - in customer facing language and product packaging. No - in legal and #regulatory classification This post is inspired by a discussion on the topic we had yesterday at the AXIAN Digibank & Fintech Annual Forum in Senegal. This is Post 1️⃣ of 2️⃣ The binding constraint in most Sub-Saharan markets is not “lack of risk”, its lack of trust, low comprehension, and high friction at the moment of value (claims). The word “INSURANCE” often encodes - paperwork, exclusions, delayed payouts, and disputes If you want penetration, you sell “protection” embedded into products people already use weekly: payments, savings, credit, merchant tools and not insurance The practical principle that I am proposing is simple: ☑️ Call it “insurance” to the regulator. ☑️ Sell it as “protection” to the customer. ☑️ Design it so the customer exp value without a PhD in policy wordings ⸻ Should #mobilemoney players pursue an #insurtech pillar? Yes - if they treat it as a distribution + claims experience business, not an #underwriting business Mobile money players should not wake up and decide to “become insurers”. They should build a #Protection pillar that does four things: 1️⃣ Bundles simple covers into high-frequency journeys (loan, savings, device, merchant acceptance, remittances) 2️⃣ Collects premiums frictionlessly (wallet auto-debit; pay-as-you-go; tiny ticket sizes) 3️⃣ Wins on claims (fast, predictable, transparent) 4️⃣ Push the innovation in the product structure (think parameteric, embedded, etc) ❌ Avoid: launching a “marketplace” of 12 insurance products. That’s a catalogue, not a penetration strategy ⸻ Where to play and who to target 🎯 Pick markets and segments where “embedded” is structurally advantaged. What does that mean? Prioritise countries/ business lines where you have: ➖ High active #wallet usage, not just registrations ➖ Existing #digitalcredit or savings motion (strongest embed points) ➖ Dense agent/merchant network (cash-in/out + servicing + trust) ➖ Regulatory clarity for #microinsurance distribution ➖ At least one capable insurer/ #reinsurer partner willing to design for digital claims SLAs Segment priority (who to target) Start where pain is frequent and willingness-to-pay is real: ➖ Digital credit users: Embed loan protection / credit life / disability cover as the default “repayment resilience” feature ➖ Mass-market families with volatile income: Embed hospital cover inside “savings goals” or “family wallet plans” ➖ Micro and small merchants (your merchant ecosystem is your moat). Embed business interruption micro-cover, fire/theft micro-cover, liability lite, and device/POS protection ➖ Gig / informal workers: Embed income #protection proxies (hospital cash, accident) tied to regular wallet activity. ➖ Remittance recipients (if you have corridors): Embed funeral/health micro-covers triggered by remittance receipt patterns Part 2 next

Insurance industry is at a tipping point. Today technologies like Generative AI, Insurance API Standardisation, and Data Mesh are opening new possibilities. But the question remains, are we seizing the right opportunities or betting on the wrong tech just because it’s new? In reality choices any insurer make today will determine whether that company will lead the future or lag behind. Generative AI is more than just a shiny tool. It opens doors that traditional AI couldn't even approach, leveraging external data to automate complex processes like underwriting and claims. But is AI the answer to everything? If your goal is seamless data exchange with business partners, APIs might be the smarter, faster route. Insurance API Standardisation avoids the costly and error-prone process of AI-based data extraction, offering accuracy and speed. Think of it as building a frictionless ecosystem where data flows freely, enhancing both customer service and operational efficiency. And we should also not overlook the risks. With AI, the stakes are high. AI Governance and Cyber Protection are non-negotiable. Newly proposed EU AI Act and rising concerns over data poisoning mean that insurers must balance innovation with compliance and security. A single breach or misuse of AI could destroy the trust that insurance fundamentally relies on. What about the human element? While we race toward AI-driven efficiency, we must ask: are we losing the human touch in an industry built on trust? AI Democratisation offers a way to bring AI closer to the everyday employee, empowering teams across all levels. But without proper training and governance, even the best AI tools can lead to biased decisions, threatening fairness and transparency. Then there’s the Generative AI paradox. It’s powerful, but if we’re just using it to swap data, we might be backing the wrong horse. APIs offer a simpler, more cost-effective solution. Real game is about choosing the right tech for the right job, for example India’s UPI, which revolutionized digital payments in a country where aiming for digital economy was considered over ambitious. Could the insurance industry see similar disruption? Ultimately future of insurance won’t be defined by AI alone but by how we integrate the right technologies : Generative AI, API Standardisation, AI Governance, into a cohesive and ethical framework to build a completely functional ecosystem . And to achieve that insurers would need to answer this critical question : "Are We Backing the Right Horse in the Insurance Tech Race?" Refer attached report for detailed insights. ⬇ #InsuranceFuture #GenerativeAI #APIsInInsurance #AIethics #TechLeadership #InsuranceTransformation #Insurtech #CyberSecurity #LinkedIn

The AI race in insurance is shifting from experimentation to implementation, and CB Insights’ hiring signals make this impossible to ignore. We identified the fastest-growing agentic AI-focused insurtechs and found that 7 of the top 9 are prioritizing implementation-focused roles. Two themes stand out: client education on AI adoption and forward-deployed engineering. These are roles designed to get AI working in production, not just in pilots. All but one of these companies raised funding since March 2025, suggesting that implementation capability has become a prerequisite for AI-focused insurtech funding. But here's the tension driving this hiring: insurtechs are doubling down on implementation in part because their customers can increasingly build in-house. CB Insights’ Hiring Insights on some of the largest insurers — including Aviva, Chubb, and MetLife — show they are moving quickly to build AI capabilities in-house. Insurance executives will increasingly expect implementation efforts to deliver measurable ROI. That bar will determine which insurtech partners win and which get replaced by in-house teams.

The Insurtech MGA Model of the Future: It's NOT All About Faster...Easier...Better Tech (Here's where most MGAs are getting it wrong) 👇 The MGA market is booming—and that’s both exciting and dangerous. Right now, venture-backed MGAs are competing to build the next big insuretech platform. They’re banking on advanced quoting systems, building great 'products' and backend efficiencies to win the market. But here’s the catch: all that fancy tech won’t save them. UNLESS...they have the right distribution strategy. My Prediction: In the next 3-5 years, many MGAs will fail. Why? Lack of focus on a winning distribution strategy and agent niche marketing, content creation and sales training support. The Opportunity: MGAs that focus on helping agents actually grow their books of business in the industry niches they serve will dominate the market. This means... 1) Creating industry niche-specific content and tools that help agents build trust, generate high quality leads, fill their sales pipeline, and drive revenue growth. INVEST in marketing, content creation and sales training to help them OWN and DOMINATE their niche. 2) Building world-class risk management programs to ensure favorable loss ratios and profitability. Give them the necessary resources and support they need to uncover problems and strategies to implement real solutions. 3) Focusing on underserved, niche markets instead of competing on speed and price. De-commoditize the market by providing industry niche expertise and a world-class client experience (for insureds). 4) Invest heavily in Producer sales and content creation training, providing resources, workshops, masterminds, and a dedicated community where everyone can collaborate and share insights, knowledge and experiences. My Two Cents... If you’re an MGA leader, now is the time to pivot. Stop chasing the tech race and start investing in the fundamentals: → Distribution: Teach agents how to market and sell your product. → Niche Focus: Build programs tailored to stable, relatively pandemic and recession-proof industries. → Risk Management: Control costs by proactively managing claims and loss ratios. → Producer Training: This is a no-brainer and self-explanatory (yet, very few are investing wisely here) Let's keep this conversation going and continue to push the limits of what's possible for the success and growth of this industry... What do you think the future holds for MGAs? Let’s discuss. Drop your thoughts in the comments!👇 #mga #insurance #insurtech #riskmanagement

One insurance gap almost wiped 40% off this insurtech's valuation overnight. Here's how we helped their CFO… The call I got from their CFO was one we get multiple times a year. They now needed deeper expertise. Like many funded startups… They put a basic insurance program in place during their friends & family round. Box ticked. Rolled it through their Series A. Three years later: In the market for a new capital raise. A matured digital I platform serving thousands of customers, and partnerships with several sizeable banks. But their insurance program was still stuck in 2021. Their risk management framework and insurance coverage were speaking different languages. And one insurance savvy investor flagged a gap during due diligence that left a major exposure unchecked. From a 30k view… Here's the insurance framework we helped them design to protect their Series B valuation: 1. Cyber Insurance → ransomware/extortion limits → tech platform interruption calc 2. Technology E&O → software failure coverage → customer data handling errors 3. D&O for Tech Companies → IPO/funding round protection → regulatory tech compliance 4. Coverage Adequacy → tech platform exposure limits → API/integration gap analysis 5. Regulatory Tech Insurance → fintech compliance coverage → digital insurance regs 6. Cost Optimization → insurtech market benchmarking → startup growth scaling costs 7. Data Liability → AI/ML decision coverage → data privacy protection 8. Policy Terms for Tech → API failure exclusions → cloud service interruption 9. Property Insurance → server/hardware protection → remote workforce coverage 10. Risk Management Services → cybersecurity programs → tech incident response 11. Emerging Tech Risks → blockchain exposure → AI liability assessment 12. Coverage Integration → legacy vs digital coverage → partner API protection 13. Claims Process → digital claims handling → real-time reporting systems 14. Policy Documentation → digital certificate system → API-based policy mgmt 15. Market Conditions → insurtech capacity limits → digital insurance trends 16. Regulatory Compliance → fintech licensing reqs → digital compliance reporting 17. Risk Transfer Alternatives → parametric solutions → micro-insurance platforms 18. Coverage Triggers → digital incident definition → automated notice systems 19. Insurance Program Structure → tech platform coverage → startup scaling structure 20. Specialized Tech Needs → open banking exposure → digital payment protection 21-25 Cont in comments… P.S. if you like this post you’ll love our newsletter. Every Friday we flag the top three articles impacting the global insurance markets. It’s for busy executives that want to stay current on the market…

🤔 𝗥𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗖𝗹𝗮𝗶𝗺𝘀: 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗛𝘆𝗽𝗲 𝘁𝗼 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻... while most carriers focus on operational efficiency — using AI to speed up existing processes — the real opportunity lies in fundamentally reshaping the cost curve itself... 𝗹𝗲𝘁 𝗺𝗲 𝗲𝘅𝗽𝗹𝗮𝗶𝗻: 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳 𝗶𝗻 𝗖𝗹𝗮𝗶𝗺𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝗺𝗮𝗸𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲 𝘄𝗼𝗿𝗸 𝗖𝗹𝗮𝗶𝗺𝘀 𝗖𝗼𝘀𝘁 𝗘𝗾𝘂𝗮𝘁𝗶𝗼𝗻:  𝗧𝗼𝘁𝗮𝗹 𝗖𝗹𝗮𝗶𝗺𝘀 𝗖𝗼𝘀𝘁 = 𝗟𝗼𝘀𝘀 𝗖𝗼𝘀𝘁𝘀 + 𝗟𝗼𝘀𝘀 𝗔𝗱𝗷𝘂𝘀𝘁𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗻𝘀𝗲 (𝗟𝗔𝗘) Loss Costs: Actual claim payouts (settlements, repairs, medical expenses) LAE: Operational costs to process claims (staff, technology, overhead) Trade-off Dynamic: Reducing LAE can increase Loss Costs if accuracy suffers; excessive LAE spending creates inefficiency 𝗧𝗮𝗸𝗲 𝘁𝘄𝗼 𝗽𝗮𝘁𝗵𝘀 𝗣𝗮𝘁𝗵 𝟭: 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝗗𝗿𝗶𝘃𝗲 𝗗𝗼𝘄𝗻 𝘁𝗵𝗲 𝗖𝘂𝗿𝘃𝗲) 𝗠𝗼𝘀𝘁 𝗶𝗻𝘀𝘂𝗿𝗲𝗿𝘀 𝗮𝗿𝗲 𝗵𝗲𝗿𝗲.. —using AI for incremental improvements: - Automated damage detection - Faster claim routing - Document processing acceleration - Fraud detection enhancement these efforts optimize existing workflows but operate within current structural constraints. 𝗣𝗮𝘁𝗵 𝟮: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝗦𝗵𝗶𝗳𝘁 𝘁𝗵𝗲 𝗖𝘂𝗿𝘃𝗲) 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗰𝗮𝗿𝗿𝗶𝗲𝗿𝘀 𝗮𝗿𝗲 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 (𝗶𝗻 𝗮𝗱𝗱𝗶𝘁𝗶𝗼𝗻 𝘁𝗼 𝘁𝗵𝗲 𝗮𝗯𝗼𝘃𝗲) 𝗶𝗻 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝘁𝗵𝗮𝘁 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗮𝗹𝘁𝗲𝗿 𝘁𝗵𝗲 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀: - Computer vision, multi-modal systems that eliminate traditional inspection needs - 3D reconstruction from customer photos - Predictive models that enable proactive claim management - End-to-end digital experiences driven by agentic AI that generate compound data advantages 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 the carriers achieving 200%+ efficiency improvements aren't just automating—they're reimagining. 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘀: - 𝗗𝗮𝘁𝗮 𝗮𝘀 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗠𝗼𝗮𝘁: Proprietary datasets become more valuable over time - 𝗛𝘂𝗺𝗮𝗻-𝗔𝗜 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Technology amplifies expertise rather than replacing it - 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Each improvement enables the next breakthrough - 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿-𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗗𝗲𝘀𝗶𝗴𝗻: Better experiences drive data generation and business growth while your competitors optimize their current processes, the question becomes: are you using AI to get better at what you've always done, or are you reimagining what's possible entirely? 𝗧𝗵𝗲 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝗻 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗵𝗮𝘀 𝗽𝗮𝘀𝘀𝗲𝗱..... 𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗯𝗲𝗹𝗼𝗻𝗴𝘀 𝘁𝗼 𝘁𝗵𝗼𝘀𝗲 𝗯𝗼𝗹𝗱 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝘀𝗵𝗶𝗳𝘁 𝘁𝗵𝗲𝗶𝗿 𝗲𝗻𝘁𝗶𝗿𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗰𝘂𝗿𝘃𝗲..... #AIinInsurance #Insurance #ArtificialIntelligence #Innovation

Recent conversations with clients and analysts really illuminated the growth of insurance #data platforms - either inhouse, or within core, and core-adjacent, software providers, or via API (as we do at Fenris Digital). Adopting an AI-first approach is better when the underlying data is accurate, accessible, and injected in real time into workflows and ML algorithms. Sourcing applicable and permissible third party data bring immense lift to results, be it at quote, renewal, or while servicing the account. This purpose built approach to data and AI directly empowers carriers, brokers, and agencies to make better and more timely decisions. But it also promotes development of a data-driven culture that prepares organizations for long-term success. In all things AI, keep in mind the guiding principles of transparent, understandable, and fair, and set the course for better customer experiences. #insurtech #propertyandcasualty #earlyriskassessment #machinelearning

Insurtech funding has been slow in 2024. No surprise there. But I'm seeing major carriers make two strategic bets: 1️⃣ Embedded insurance Key advantages: → Offering convenience through point-of-sale integrations → Overcoming consumer inertia at the moment of need 2️⃣ Cybersecurity Dual strategy: → Expanding into cyber insurance as a new revenue stream → Strengthening internal defenses against rising digital threats KPMG data confirms what I've been seeing in the market. MGAs that lead with tech are still attracting capital (“tech trumps fin”)—they have proven business models and don't carry risk on their balance sheets. That makes them attractive acquisition targets for carriers. AI is gaining traction, particularly for fraud detection and risk assessment and corporate investors are tightening wallets to focus on AI enablement. Insurtech shows promise for the rest of 2024, heading into 2025. The carriers that nail the trifecta—AI, embedded distribution, and cyber—will define the next generation of insurance... and I'm watching closely.