Implementing an AI-Driven Risk-Management and Fraud-Prevention System for Real-Time Claims-Fraud Detection in Property and Casualty Insurance: A Simulation-Based Evaluation

Abstract

Background. Property and casualty insurers score claims for fraud risk under conditions that shift as organized staged-loss rings adapt, genuine claims behaviour evolves, and seasonal catastrophe activity moves claim volumes. Guria et al. argue that AI-driven risk management and fraud prevention in digital finance is a catalyst for economic resilience, but their contribution is conceptual and offers no insurer-specific quantitative evidence of what such a system delivers relative to a conventional, non-adaptive scoring approach. Methods. This study implements an AI-driven, continuously updated, SHAP-explainable claims-fraud scoring system, following the risk-management and fraud-prevention approach set out by Guria et al., and compares it against a legacy static scoring policy over five independent replications of a 52-week, 135,200-claim synthetic stream containing a sudden staged-collision ring, a gradual shift in genuine claims behaviour, and a storm-season claims surge. Results. The legacy static policy's recall at a 1% review budget fell from 12.8% at baseline to 8.6% during the staged-collision ring and never recovered; the AI-driven system held recall at 12.8% during the same event and delivered an overall average precision of 0.302 against the legacy policy's 0.231, a 31% relative improvement, while SHAP analysis traced the ring's detection specifically to staged_loss_pattern_score, a feature that carried negligible weight before the ring began. Conclusion. For a property and casualty insurer, this evaluation provides simulation-based quantitative support for Guria et al.'s argument that an AI-driven, continuously updated, explainable risk-management approach measurably strengthens resilience to emerging fraud patterns relative to a static legacy policy, at the operational cost of weekly retraining that an insurer's model-governance programme must accommodate.