Customer Risk Profiling System

Abstract

This thesis addresses the critical need for advanced, ethical risk quantification in the motor insurance sector, currently hampered by fragmented data and limited cross-company fraud visibility. The primary objective was to design and validate a Customer Risk Profiling System (CRPS) that integrates heterogeneous data sources and utilizes Machine Learning (ML) for dynamic risk scoring. The methodology involved aggregating data streams including claims, premiums, policy history, and external PEP/AML compliance scores and employing Gradient Boosted Trees (GBTs) to achieve a high classification Accuracy of $0.7561$ and a ROC-AUC of $0.8982$. Empirical findings confirmed the predictive power of behavioral features over linear demographic metrics, validating the choice of non-linear ensemble models. The CRPS successfully segments customers into Low, Medium, and High-Risk tiers, enabling targeted intervention. Crucially, the system embeds Explainable AI (XAI) using SHAP values and a continuous Feedback Loop to maintain accuracy against concept drift, ensuring auditability and ethical governance against potential bias. The study concludes by proposing the Insurance National Grid (NIG), a centralized platform designed to connect all insurers to the regulator. The NIG would enforce data standardization and enable cross-company fraud detection, magnifying the CRPS's impact from a firm-specific tool to a national strategic asset, thereby promoting market efficiency, compliance, and sustained sector resilience.