Testing What Matters Most: Leveraging AI Potential in Personal Lines Insurance (Auto & Home) Testing

Abstract

Driven by the use of artificial intelligence (AI) in product creation, underwriting, and, increasingly, software testing, the insurance sector is changing tremendously. Effective testing of AI-powered systems is especially important within personal lines insurance—namely auto and home—where speed, accuracy, and customer-centricity are essential. With an eye on personal lines products, this article investigates the strategic role artificial intelligence has in improving the quality assurance and testing procedure of core insurance platforms. We contend that testing what matters most—such as pricing accuracy, claims automation, and risk modeling—calls not only on conventional QA techniques but also the use of AI-enhanced testing frameworks.
The study specifies main testing objectives and suggests a thematic framework for maximizing test coverage, data quality, regulatory compliance, and model interpretability in artificial intelligence systems employed in personal lines by examining both academic literature and industry practices. The study emphasizes the use of self-healing scripts, AI-driven test case generation, anomaly detection in model results, and understandable artificial intelligence (XAI) integration to guarantee openness in underwriting decisions. Case studies of insurers using artificial intelligence for vehicle and residential insurance solutions show notable efficiency improvements as well as new difficulties, among which are algorithmic bias and data drift.
Ultimately, the paper underlines a “shift-left” testing strategy—incorporating early AI testing in the software life cycle—to help proactively reduce risk and improve customer outcomes. Insurers may drive both confidence and technological agility in a competitive market by matching AI testing with what matters most in auto and home insurance.