Citation:

Kiran Babu Boddapati, 2026. AI -Driven Regression Testing for Policy Lifecycle Scenarios in Multi-Line P&C Insurance   Volume 6 Issue 2: 124-134.

Abstract:

AI-assisted regression testing is beginning to transform enterprise insurance systems, especially property and casualty platforms in which policy-lifecycle behaviour spans quoting, binding, endorsements, renewals, reinstatements, cancellations, billing interactions, and claims-related updates across multiple product lines. In this environment, product variation, rule volatility, regulatory constraints, and workflow interdependence increase release risk in ways that cannot be adequately managed through static test inventories alone. This review analyzes literature relevant to AI-based regression testing in multi-line P&C insurance, bringing together the existing literature on regression test selection and prioritization, model-based and combinatoric testing, process mining, predictive process monitoring, explainable AI, and the digitalization of insurance. Major themes include scenario explosion, business-process conformance, risk-based prioritization, data quality, lifecycle-aware test generation, and governance in high-stakes change management. The reviewed studies suggest that AI-assisted regression strategies can improve defect-detection efficiency and test-planning support, although important gaps remain in domain-specific analysis, longitudinal study within policy-administration systems, and integration of process semantics with learned prioritization logic. This topic is becoming increasingly important because insurers now rely on rapidly changing policy platforms whose defects can propagate across underwriting, billing, compliance, and customer service with significant operational and regulatory consequences.

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Keywords:

Artificial Intelligence; Insurance Systems; Policy Lifecycle; Property and Casualty Insurance; Regression Testing