AI: achieving a balance of efficiency, accuracy in in life insurance underwriting

By Laura McKiernan Boylan

Artificial intelligence is steadily emerging as a vital tool in the life insurance industry. AI enables life insurance underwriters to navigate a wealth of information more efficiently, automating workflows and refining risk assessments to improve consistency and operational speed.

Generative AI models and large language models are gaining attention for their ability to analyze large datasets, identify patterns and synthesize data – often more efficiently than humans – while supporting data-driven decisions. Despite these advancements, GenAI and other AI applications are not replacements for human underwriters and their nuanced expertise. Instead, they serve as a complementary tool, working alongside skilled risk experts to enhance processes and improve outcomes.

By combining human expertise with AI’s processing power, carriers can make progress in achieving a balance between efficiency and accuracy in underwriting. This approach enables the development of strategies that harmonize emerging technologies, such as generative AI, with the critical insights and judgment of human underwriters.

Data in underwriting

Life insurance underwriting has long relied on diverse data sources such as electronic health records, medical claims and behavioral data. Over the last decade, the volume and quality of available data have grown exponentially. Although this data is incredibly rich and valuable, the industry is still working to optimize its use to improve outcomes and funnel performance, often requiring expert analysis to unlock its full potential.

Incremental progress is being made as AI solutions improve the industry's ability to assess data relevance and quality. For example, while granular details like an applicant's allergy history or eyeglass prescription are accessible, not all data points contribute meaningfully to assessing risk and creating a policy. Identifying the right insights requires a combination of sophisticated algorithms, human expertise and prudent analysis.

After AI tools aggregate data, underwriters filter out irrelevant information and distill complexities into precise, actionable conclusions to inform a policy. Under the right circumstances, AI can fully drive decision-making. But it still requires careful oversight and rigorous review before being deployed in production, given the unique challenges of the industry – managing very low-frequency, very high-severity losses. This shift demands advanced technological integration and heightened human oversight to ensure accurate and reliable outputs.

AI: Balancing efficiency and accuracy

AI enhances underwriting efficiency by automating labor-intensive tasks such as data collection and initial risk assessments. Predictive analytics and GLMs allow carriers to rapidly process vast datasets, uncover patterns and accelerate decision-making. These tools transform traditional workflows, enabling underwriters to focus on complex cases and improve consistency. Advances in generative AI have the potential to take this even further, driving automated decision-making on medical records at the same level – or eventually beyond – that of human underwriters.

However, the ultimate goal is not simply efficiency but achieving accuracy alongside efficiency. Delivering decisions as nuanced and reliable as those made by experienced underwriters requires a blend of innovation and human expertise.

Implications for policyholders

Policyholders stand to gain tangible benefits as carriers continue to build out AI-influenced underwriting strategies. Faster underwriting processes mean quicker approvals and reduced waiting periods for coverage. Enhanced accuracy ensures fairer pricing, aligning premiums with individual risk profiles while expanding personalized coverage options to meet a wide range of needs.

Moreover, underwriting with AI components can broaden access to underserved markets by providing a more nuanced understanding of risk. This fosters inclusivity and widens access to life insurance, particularly for younger customers or those with a thinner data footprint. This also democratizes financial protection and drives financial security.

What’s next for AI?

The life insurance industry is at a critical juncture as AI capabilities evolve. Emerging models such as GenAI offer opportunities to refine decision-making processes further. However, the challenge lies in integrating these innovations responsibly – balancing efficiency, accuracy and accessibility while addressing ethical and regulatory considerations.

Looking ahead, 2025 could mark a tipping point where advances in data quality and AI maturity enable significant progress in underwriting. Carriers can navigate this evolution by adopting a measured and data-centric approach with human oversight and designing systems that seamlessly scale once the tipping is reached. This ensures they build trust with regulators, policyholders and partners while delivering intelligent, well-informed underwriting decisions.