GenAI Use Cases: Insurance
Insurance companies provide risk management services by pooling premiums from individuals and businesses to offer financial protection against losses such as accidents, illness, property damage, or death. It plays a vital role in economic stability by helping entities transfer risk and recover from unexpected financial setbacks.
Key players: US Insurance Leaders (State Farm, Berkshire Hathaway, Progressive, Liberty Mutual), Reinsurers (Berkshire Hathaway Reinsurance Group), Insurtech (Lemonade, Oscar Health)
The insurance industry is integrating AI across the policy lifecycle, and now exploring generative AI to enhance those capabilities. Underwriting has become increasingly algorithmic: life insurers use predictive models to offer instant issue policies (no medical exam) by analyzing medical records and third-party data with AI – a process called “accelerated underwriting.” Some personal auto insurers have shifted to usage-based insurance (UBI) models, where mobile telematics data from your car (using GPS data, accelerometers, and sensors on phone or in car) is analyzed by ML algorithms to continuously adjust premiums to your behavior and risk profile. Regulators scrutinize these models to ensure they are fair and not proxies for prohibited factors (for example using driving data is fine, but it shouldn’t inadvertently charge more just because of where someone drives, which could correlate with income or race). Explainable AI is therefore a focus in insurance, with companies adopting AI techniques that can provide reason codes for decisions to satisfy regulatory filings. On the claims side, AI is approaching an end-to-end role: from first notice of loss (FNOL) where a chatbot collects the details, to adjudication where computer vision and databases verify the claim, to payment where an algorithm decides the amount – all in a matter of minutes for simple cases. More complex claims (house fire, major injury) still involve human judgment, but AI assists those humans by organizing information, highlighting discrepancies, and even forecasting the likely severity of a claim early on (BCG). Insurers are also using AI in risk management, for example, analyzing satellite imagery with deep learning to assess wildfire or flood risks for properties (and suggesting mitigation strategies to policyholders).
With GenAI, insurers are finding new ways to enhance efficiency and customer engagement. One promising use is summarizing lengthy documents: insurance policies, claims descriptions, legal reports, etc. An adjuster or underwriter can input a pile of text (like hospital reports for an injury claim), and an LLM can produce a concise summary or extract key points (a process dubbed “Generative Summarization”), saving hours of reading. GenAI can also draft customer-facing communications. For instance, if a claim is denied or a policy is canceled, a finely tuned language model can draft an explanatory letter tailored to that customer’s situation – something traditionally done by agents using templates. Some insurers are even exploring AI-generated training: feeding past examples of, say, fraudulent claims to a generative model to create new simulated cases on which to train investigators or to stress-test fraud detection systems. Lastly, insurance product design might benefit from AI creativity: by analyzing market data and customer feedback, AI could suggest new coverage options or pricing structures that humans might not have thought of such as on-demand insurance models or micro-insurance for specific short-term needs (LeewayHertz) (Deloitte).
Throughout these innovations, the balance of automation and human oversight remains critical. Insurance is a heavily regulated industry with consumer protection at its core. Thus, a turning point in recent years has been the collaboration between regulators and industry on AI guidelines. The National Association of Insurance Commissioners (NAIC) has convened working groups on Big Data and AI, aiming to ensure that as companies deploy AI, they do so with transparency, fairness, and accountability. Insurers are creating ethics boards for AI and adopting frameworks to test for bias (for example, making sure a ML model isn’t unfairly denying claims from a certain demographic). Despite these cautionary steps, the trajectory is clear: AI has become indispensable in insurance for improving accuracy and speed. Every major insurer now has an innovation lab or data science team driving AI projects, ensuring that the evolution continues in the coming years, with the promise of more personalized, efficient, and fair insurance services for consumers.
Notable start-ups currently building insurance-specific applications include: Sixfold (risk assessment tool built for underwriters), Roots Automation (Created InsurGPT — insurance market-focused LLM), Dais Technology (developed UnderwriteGPT to accelerate underwrite and risk management processes).
