An Enhanced Focal Loss Function to Mitigate Class Imbalance in Auto Insurance Fraud Detection with Explainable AI

TL;DR

This paper introduces a three-stage training framework using an enhanced focal loss to address class imbalance in auto insurance fraud detection, improving detection accuracy and interpretability.

Contribution

It presents a novel structured training approach combining convex and non-convex losses with explainable AI techniques for fraud detection.

Findings

Improved minority-class F1-scores and AUC over baseline methods

Enhanced feature discrimination through the proposed loss framework

Provides interpretable feature attributions with SHAP analysis

Abstract

Detecting fraudulent auto-insurance claims remains a challenging classification problem, largely due to the extreme imbalance between legitimate and fraudulent cases. Standard learning algorithms tend to overfit to the majority class, resulting in poor detection of economically significant minority events. This paper proposes a structured three-stage training framework that integrates a convex surrogate of focal loss for stable initialization, a controlled non-convex intermediate loss to improve feature discrimination, and the standard focal loss to refine minority-class sensitivity. We derive conditions under which the surrogate retains convexity in the prediction space and show how this facilitates more reliable optimization when combined with deep sequential models. Using a proprietary auto-insurance dataset, the proposed method improves minority-class F1-scores and AUC relative to conventional focal-loss training and resampling baselines. The approach also provides interpretable feature-attribution patterns through SHAP analysis, offering transparency for actuarial and fraud-analytics applications.