From Traditional to AI-Driven Risk Assessment: An SPSS-Based Statistical Investigation of Machine Learning Applications in Property and Casualty Insurance

Abstract

The property and casualty (P&C) insurance industry is undergoing a transformative shift from traditional rigid risk assessment methods to AI-driven predictive models. This study examines the integration of artificial intelligence and machine learning technologies in insurance risk management, moving beyond conventional actuarial models that are often linear, paper-based, and time-consuming. The research focuses on how AI can facilitate continuous, real-time risk assessment and monitoring, particularly in healthcare, where personalized care and precision medicine are gaining importance. Key variables analysed include AI integration levels, insurance types, data sources, regional variations, regulatory readiness, customer demographics, risk prediction accuracy, claims fraud detection capabilities, cost reduction potential, customer trust factors, and compliance levels. The method uses SPSS statistical analysis to explore these multifaceted relationships across different insurance sectors and geographies. The findings indicate that AI-powered systems significantly improve the effectiveness of risk management approaches by providing immediate alerts to anomalies and emerging threats, while improving fairness and transparency by reducing human biases associated with traditional methods. This technological advancement represents not just an operational improvement, but also a strategic paradigm shift in how insurers perceive, access and manage risk in today’s rapidly evolving digital world. Keywords: Artificial Intelligence, Property and Casualty Insurance, Risk Assessment, Machine Learning, Predictive Analytics, Claim Fraud Detection, RealTime Monitoring, SPSS Analytics, Insurance Technology and Operational Models

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