AI in Financial Services

But what if insurance worked more like Netflix? Netflix tracks your viewing behavior and adapts recommendations instantly. If insurance products adapting the same way, premiums adjusting dynamically to fitness levels, coverage expanding with life stages, benefits rebalancing as goals evolve. McKinsey estimates AI-led personalization could lift insurer revenues by 10–15%, while lowering claims costs through early risk detection. And The technology already exists. Wearables generate 250+ daily data points per user around heart rate, sleep, activity. PwC reports 63% of consumers are willing to share health data if it results in cheaper or more personalized premiums. And Personlaized premiums is not a distant reality. It can be achieved by: 𝟏. 𝐈𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 that allow secure ingestion of health and behavioral data at scale. 𝟐. 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐬𝐚𝐧𝐝𝐛𝐨𝐱𝐞𝐬 that encourage innovation while protecting privacy. 𝟑. 𝐀𝐈 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 to ensure transparent pricing and avoid hidden bias. 𝟒. 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩𝐬 with health-tech, fintech, and wellness players to broaden value delivery. Insurance is likely evolve from a once-in-a-decade purchase to a living product. #DigitalIndia #Fintech #AI #technology #Fintech #AI #technology

What is the current level of AI adoption in insurance? That's what KPMG explores in this report. The 40-page document starts with a reminder of the technology itself, split between AI and Generative AI. Then it highlights several use-cases where these technologies could benefit the insurance value chain (e.g. actuarial processes, page 10) to automate routine tasks, enhance underwriting accuracy and personalize customer interactions. It also tackles the make or buy dilemma, unveiling that almost half of insurers have kicked-off internal initiatives so far, mixing business and tech employees (see page 13). KPMG also offers a framework to assess incumbents' maturity levels in these AI roadmaps (see page 19). And of course, there is a full section dedicated to risks c-level perceive from these AI technologies, starting with finding the right balance between technology and... people (starting at page 29). In case you don't have time to read the full report, I highly suggest you have a look at page 3 which summarizes key learnings. Basically: 1/ If insurers are running many initiatives, it takes time to switch from trial to production. 2/ Incumbents need to find the right balance in risk taking: innovation requires taking risks while regulation requires to meet standards. 3/ Key success factors stand in both data management and mastering HR challenges. ✍️ If the roadmap is not a surprise - enhance data management and balance HR & tech requirements - I found the overview quite interesting as it lists use-cases incumbents have already initiated and shares figures about where the market stand in terms of both adoption, perspectives and fears. This should help startups - and investors - spot opportunities and faster go-to-market strategies ! #insurance #insurtech #artificialintelligence

The Insurance Industry Is at an Inflection Point – and AI Is Leading the Charge From outdated systems and unstructured data to rising customer expectations and talent shortages — insurers are under immense pressure. But with Generative AI, there’s finally a real way out. What’s Changing? 1. 60% of operational costs are still manual – AI can slash that. 2. 80% of data is untapped – GenAI reads, learns, and leverages it. 3. Only 18% of insurers currently use AI – but that’s about to change. Key Impact Areas: ✅ Underwriting: 90% data accuracy + new product models. ✅ Claims: 70% of simple claims can be auto-resolved + up to 50% faster processing ✅ Customer Experience: 48% higher NPS, 85% faster resolutions ✅ Fraud Detection: AI flags 75% of fraudulent claims in real time ✅ Sales & Distribution: AI agents, personalized funnels, smarter upsells ✅ Policy Admin: Real-time compliance, automated changes, predictive lapse alerts ✅ New Products: From behavior-based insurance to once “uninsurable” tech like drones & autonomy It’s not just about automating workflows. It’s about rethinking the very DNA of insurance using AI-first foundations. And those who don’t adapt — risk becoming obsolete. Whether you're transforming an incumbent or building the next vertical AI unicorn — the time is now.

Can Insurance Employ AI That Is Both Powerful and Fair? Artificial intelligence is rapidly reshaping how insurance companies process claims, detect fraud, and manage risk. But to be effective and fair, AI must be developed and deployed with careful attention to data quality, model transparency, and ethical use. AI systems are only as good as the data they are trained on, and if that data is biased or incomplete, the outcomes will reflect and even amplify those problems. In a conversation filled with lived experience, John Standish⁠, Co-Founder and Chief Innovation and Compliance Officer at Charlee.ai, laid out a powerful and pragmatic vision for how artificial intelligence must be built for the insurance industry. Having transitioned from a long and substantial career in law enforcement and insurance fraud investigations to the world of InsurTech, John offers rare dual expertise: a regulator’s scrutiny and a technologist’s curiosity. His perspectives cut through hype and buzzwords and land squarely in the domain of real-world consequences, compliance, and human-centered innovation. John underscored the importance of domain-specific AI models that are trained with relevant, clean, and unbiased data. He cautioned against using generic models and stressed the need for explainability, transparency, and regulatory compliance in all AI-driven decisions. The conversation illuminated a crucial point: AI isn’t a magic fix for outdated processes—it’s a force multiplier for organizations willing to rethink their foundational data strategies and workflows. For the insurance industry, embracing this challenge is not just a matter of innovation, but of survival in a rapidly changing digital landscape. #technology #innovation #frauddetection #claimsmanagement #artificialintelligence #insurance #insurtech Look for the full YouTube episode in the comments.

Here's my 2024 LinkedIn Rewind, by Coauthor.studio: 2024 marked a pivotal moment for AI in insurance - moving from theoretical discussions to practical implementation. The launch of the AI Act, major strategic partnerships like AXA-Mistral AI, and real productivity gains of 38% in insurance operations showed that AI's impact is now measurable and meaningful. Through 85+ episodes of "Inno and Tech" podcast on Bretagne5 and extensive conference engagements, three key transformations emerged: 🔹 The integration of AI moved from pilot projects to strategic implementation, with major insurers showing clear productivity gains while carefully navigating governance requirements 🔹 Large language models evolved from general tools to specialized insurance applications, particularly in underwriting, claims processing, and customer service 🔹 The focus shifted from pure automation to augmented intelligence, with human expertise remaining central to decision-making Most impactful posts from 2024: "La pyramide des salaires en France" Data-driven analysis of salary distribution providing valuable market insights https://lnkd.in/es9nbjZy "Breaking news // C'est officiel, le texte de l'IA Act" Breaking down the EU's landmark AI regulation and implementation timeline https://lnkd.in/eBWUUAXh "Joue-la comme Ikea!" Analysis of strategic partnerships reshaping insurance through AI https://lnkd.in/eBkqaHU4 Looking ahead: 2025 will be shaped by the practical implementation of the AI Act, evolving partnerships between traditional insurers and AI companies, and the continued focus on responsible innovation. The Paris AI Summit in February will be particularly significant for setting the global direction of AI governance. To everyone working to thoughtfully integrate AI into insurance: the real work of balancing innovation with responsibility is just beginning. -- Get your 2024 LinkedIn Rewind at https://lnkd.in/eWcyDN3E

AI is transforming insurance! Here’s how: Generative AI is revolutionizing predictions. With 34% of insurers finding it most effective in predictive analytics and which in turn now enables better demand analysis, ensures companies are prepared for market changes. Automated customer advice is another game-changer. Personalized experiences are now possible, enhancing customer satisfaction and loyalty. Natural language processing (NLP) and voice recognition improve underwriting processes, making them faster and more accurate. Fraud detection has seen significant advancements with AI-driven image recognition. This technology helps identify suspicious activities quickly, reducing financial losses and enhancing security. Productivity has notably increased in countries like Germany, Spain, and Austria. A 0.5% boost in productivity can lead to a 1% decrease in labor costs. This is crucial as the EU-27 workforce is expected to shrink by 20% by 2050 due to an aging population. Contrary to popular belief, AI is not a job killer. Allianz Research shows AI is more likely to boost productivity and skills rather than cause mass job losses. AI can help address labor shortages and aging workforce challenges. AI in insurance is about balancing innovation with regulation. It’s about leveraging AI’s benefits while addressing concerns. The goal is to enhance efficiency, improve customer experiences, and maintain robust security. If you’re in the insurance sector and want to harness the power of AI, let’s talk. Our team at CellStrat is here to help you navigate this transformation and solve your unique challenges. Reach out to us today for a consultation!

Still trying to justify your AI investments using a 3-year ROI model? That approach belongs to the ERP era—not the age of AI. A mid-sized insurance advisory firm I worked with recently hit this wall. They had a game-changing vision: use NLP to transform risk assessment. But the CFO kept asking for a traditional ROI template. Here's what we did differently—and why you should too. We scrapped the "financial return over 3 years" narrative. Instead, we created a 90-day PoC to demonstrate insight value: ✔️ Reduced manual review time by 35% ✔️ Increased quote speed by 22% ✔️ Detected risk factors humans missed We built a case around learning return and value of insight (VOI)—metrics far more relevant for AI than fixed IRR and NPV calculations. 🔄 AI doesn’t fit the clean predictability of IT systems. AI is messy, emergent, and requires a co-owned approach between business and tech leaders. CxOs must now: ✔️ Replace rigid ROI models with phased, agile frameworks ✔️ Focus on insights as early wins, not just financial metrics ✔️ Build trust by educating boards on how AI learns before it earns ✔️ Stop retrofitting AI into legacy thinking 📣 If you're building an AI business case this quarter—let’s talk. I’ll walk you through what worked, what failed, and how to pitch the story that actually lands. #AILeadership #DigitalDecisionMakers #CxOStrategy #AIExecution #TechLeadership #AgileStrategy #AIinInsurance #ProfessionalServices #FutureOfBusiness #ValueOfInsight #EmergentTech #BusinessAgility #AIUseCases #EnterpriseAI #DataDrivenLeadership #PASH

This one’s for Founders & Sales folks. I built an AI agent that cut my sales follow-up time by 90%. Not kidding. From 30 minutes per email... to 2 minutes. And I actually enjoy it now. Let me back up. I hate writing sales follow-ups. → Re-reading call notes → Trying to remember context → Spending hours wordsmithing Even with my system of organized ChatGPT folders with custom deal context, it still took forever. So I did what any founder would do. I built a tool. It sounds much harder than it actually was. I hadn’t built an AI agent before and it only took me 2 hours end to end. Here’s what I used and how it works. ⚙️ Built with: Relay.app (shoutout to Jacob Bank - love what you’re building!) Step 1: I trigger Relay to follow up with a particular deal in Hubspot. Step 2: Relay retrieves deal context from Hubspot (it’s made me much more diligent about making sure my data is up-to-date here) Step 3: Agent reviews the deal and decides if a follow-up is needed. It gives me the following output: Is a follow up required? Yes / No response What kind of follow-up is required? General check-in email, breakup email, nudge with resources (I provided these options for it to choose from). Why did it make this decision? This is really helpful because it gets me up to speed on the deal quickly—when did we last check in, what were their objections or concerns, when is the next expected touch point, and so on. Step 4: I approve or tweak. I tell the agent if it’s right or wrong, or provide context it may not have. Step 5: AI writes a draft email. The first draft hits me within ~20 seconds. I give high-level feedback (e.g., “focus more on timeline urgency”) if necessary. Step 6: AI revises the draft based on by input. At this stage I have an almost perfect draft. I make minor edits if at all and hit send. The whole process takes 2–3 minutes max. Are we all getting replaced by AI in 2 years? Probably. But for now, I’ve outsourced an annoying part of sales and it's amazing.

Ready for takeoff: Generative AI in insurance Boards at every insurance company are talking about gen AI. But the discussion has changed from POCs to now rapidly executing ideas for responsible, secure, scalable, and commercially successful gen AI. The direction of travel !! Some insurers are already using gen AI in the back office for tasks like knowledge management. But since insurance is all about probability & statistics, we expect to see it soon across the entire enterprise. The next wave of deployment will include areas like risk scenario modelling & enhancing cognitive processes (alongside AI and RPA) where human intervention was previously necessary. Customer-facing uses are being created and we expect insurers to use gen AI to understand customer preferences and drive personalized products and services. First things first  For a successful gen AI-led transformation, insurers need a well-planned and well-communicated  change roadmap made by a cross-functional team, from an enterprise-wide point of view. At this stage, leaders would be well-advised to develop an ecosystem of partnerships to share gen AI expertise, since there is serious competition for capable talent. Tackling data demands  Data is the greatest challenge to getting gen AI right, since all generative large language models rely on high quality data and excellent prompt engineering for their success. Insurers will need to make sure that the way they train their gen AI models is transparent, fair, and accountable. This means knowing where their data comes from, where it’s housed, how secure it is, and whether their planned uses are ethical and responsible under todays’ data laws. To train gen AI models effectively, they will have to put old customer data into today’s context and use synthetic data to overcome gaps in their data that could lead to bias, as well as look for potential unfair correlations with external data sets that could deliver poor outcomes. Keeping compliant   The data challenge is where regulators are focusing their attention. Already there are laws in some US states (Colorado & California), and in Europe, that require insurers to, e.g., backtest some gen AI-delivered outcomes. And then there are industry agnostic laws governing gen AI, that capture insurers too, e.g. use of external consumer data. Expect regulation to get tighter and more specific. The regulation requirements need not be considered adversarial. Instead, they should be prepared to answer on data lineage, audibility, and governance structures.  As insurers begin to implement gen AI across their business, it is important to focus on fair & transparent outcomes, build a strong data foundation, and partner with expert vendors to help them achieve their goals.  ... But it isn’t all challenge and competition, insurers should feel positive that Gen AI can help them to better deliver for and delight their customers. Ben Podbielski Ramesh Sethi Maria Kokiasmenos Genpact

The insurance industry has been promising revolutionary change since the early 2010s ⌛ Your smart home would know when a pipe was about to burst and shut off the water before you knew there was a problem. As you locked the front door, your insurance would seamlessly shift from home to motor, adjusting your premium in real time based on road conditions, your driving history, and the weather. Every conference presentation showed the same timeline: "3-5 years away." 2015 came and went. Then 2020. Now we're halfway through 2025, the "blue sky thinking" sessions have fizzled out, and the industry has learned to be more cautious with timelines. But the fundamental challenge remains: we're still not delivering the transformation the industry keeps promising. What's different this time? AI has reached the capability threshold needed to handle insurance's complex, unstructured data reality. 👉 5 insurance AI applications that I'm genuinely excited about: ↳ End-to-end claims automation - you crash at 3am, AI handles everything overnight, you wake up with repairs booked and money transferred ↳ Intelligent fraud detection - AI spots fake damage photos, synthetic identities, and coordinated fraud rings operating across multiple insurers ↳ AI broker assistants - AI agents that simultaneously negotiate with multiple insurers, optimising your renewal terms automatically ↳ Cross-carrier fraud networks - AI systems that share intelligence across the entire industry ↳ Zero-friction underwriting - AI pulls from hundreds of data sources to assess risk instantly without you filling out anything The reality today? Only 11% of UK insurers report successful AI outcomes. Over 50% of pilots stall because of data quality issues. The winners by 2030 won't be the companies with the most cutting-edge AI - they'll be the ones who make it work consistently. The gap between promise and reality is still enormous. But for the first time in years, I'm genuinely optimistic we might finally start to close it. Are you seeing real AI progress in your industry, or is it still mostly hype? 👇