Abstract

As life insurance companies increasingly deploy artificial intelligence (AI) for underwriting, pricing, claims processing, and customer engagement, ensuring the reliability, fairness, and regulatory compliance of these models becomes critical. AI systems evolve over time, often exhibiting subtle changes in decision-making—referred to as behavioral drift—that may not be detected by traditional performance metrics. For instance, two similar policyholders might receive different underwriting outcomes due to gradual, unintended model shifts. Behavioral drift poses significant risks in regulated environments, including compliance violations, reputational damage, and erosion of customer trust.
This paper introduces the Behavioral Drift Testing Framework (BDTF), a domain-specific methodology designed to detect, analyze, and mitigate behavioral drift in life insurance AI models. BDTF integrates synthetic personas, historical replay, counterfactual testing, fairness benchmarks, drift signatures, threshold monitors, and retraining guardrails into a cohesive, lifecycle-driven approach. We demonstrate its effectiveness through case studies in underwriting, claims triage, and premium recalibration, highlighting how BDTF identifies and corrects subtle behavioral shifts. The framework offers insurers a practical, repeatable method to ensure AI-driven decisions remain fair, consistent, and compliant over time.