AI in Insurance: Underwriting’s Future

Artificial intelligence (AI) is rapidly transforming the insurance industry, especially in underwriting—the critical process where insurers assess risk and determine premiums. As AI becomes integral in reducing costs, enhancing accuracy, and accelerating workflows, its impact on underwriting is poised to redefine the future of the industry.

AI Adoption in Underwriting Is Accelerating

AI adoption in underwriting has surged across the industry. According to Morgan Stanley, the number of insurance companies leveraging AI increased from 48% to 71% between January and mid-2025 . A recent technical analysis published in BizTech Magazine reveals that for standard policies, AI has brought underwriting decision times down to just 12.4 minutes, while maintaining an accuracy rate of 99.3% . These numbers reflect both the speed and precision gains unlocked by AI.

Global data confirms this trend. A WiFiTalents report shows AI-driven underwriting can reduce processing times by up to 50%, with 48% of insurers already using AI to automate underwriting decisions . It also highlights that 80% of insurers expect AI to substantially improve underwriting accuracy by 2025 .

Efficiency Gains and Market Impact

Operational efficiency is one of AI’s strongest advantages. In the commercial insurance sector, 65% of companies report improved customer experience through AI, while underwriting costs have been reduced by up to 30% . Similarly, AI implementations have already become core to strategic decision-making and digital transformation across the industry. The global AI insurance market is projected to swell to $10 billion–$22 billion by 2025, depending on the sector .

Real-world use cases illustrate this vividly. For example, Markel achieved a 113% increase in underwriting productivity, reducing quote turnaround from 24 hours to 2 hours using AI . Haven Life, a direct-to-consumer life insurer, can issue policies in just 20 minutes, a significant improvement over traditional timelines .

AI Accuracy, Fraud Detection, and Risk Assessment

AI is enhancing underwriting quality through improved risk modeling and fraud detection. Machine learning models have improved predictive accuracy by 25%–30%, helping match premiums more closely to risk . Fraud detection is also becoming more effective: AI systems can catch fraud with 85%–90% accuracy, reducing false positives and billions in annual fraud-related losses .

Additionally, AI’s predictive analytics allow insurers to forecast claims trends, manage reserves, and reduce loss ratios. Over 70% of insurers use AI for predictive analytics, while AI underwriting is credited with increasing profitability by around 20% .

Augmenting Human Underwriters, Not Replacing Them

While AI delivers real gains, leading insurers emphasize that AI is a tool—not a replacement for people. QBE, for instance, uses AI assistants to streamline underwriting workflows, helping staff increase productivity by 10%–50%. These tools sift broker data and format it for humans to review, thereby avoiding the need to hire 25% more underwriters as volumes grow .

Similarly, AIG’s leadership under CEO Peter Zaffino has pushed the integration of GenAI and large language models into data ingestion and decision-making processes, resulting in a 15 percentage point increase in underwriting data accuracy .

Generative AI and Personalized Underwriting

Generative AI (GenAI) is shaping future scenarios in underwriting. Over 40% of insurance executives already see its positive impact on customer experiences and productivity, with 89% planning increased investment in GenAI applications . GenAI enables hyper-personalization of policy prices and service, rapidly analyzing customer histories, behaviors, and context to tailor offerings.

DYK

Ethical Considerations: Bias, Privacy, and Explainability

AI models must be built and managed ethically. Underwriters report concerns about complexity and data integrity—fewer than half (43%) accept automated suggestions outright, citing overcomplex systems and trust issues . Modern AI underwriting must address these concerns with transparency and explainability.

Academic research also highlights fairness and privacy. Surveys find that consumers deem behavioral underwriting fair only if they can influence outcomes—for example, usage-based car insurance is viewed favorably if discounts are tied to driving behavior . NLP and explainable AI frameworks are emerging to make AI underwriting decisions interpretable and trustworthy .

Looking Ahead: The Future of Underwriting with AI

The future of underwriting lies in AI-human collaboration and continuous innovation. Over 90% of insurers are expected to incorporate AI into core systems by 2026 . AI can automate up to 45% of policy issuance, reduce underwriting errors by 30%, and help tap into new markets 30% faster .

Forecasts suggest that AI will transform industry structures by 2030—80% of insurers believe AI will reshape the sector fundamentally . The key will be combining AI’s speed with human oversight, ethics, and regulatory compliance to build a resilient, customer-centric future.

Conclusion

AI is revolutionizing insurance underwriting—making it faster, smarter, and more efficient. From reducing processing times from days to minutes to increasing accuracy and reducing costs, AI is empowering insurers to deliver better risk assessments and tailored pricing. As AI integrates further, the most successful insurers will be those that blend technology with human expertise and ethical frameworks. Underwriting’s future is not replacement, but augmentation—guided by AI, shaped by humans.