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

2025-08-04

AI Summary

To address severe class imbalance in automobile insurance fraud detection, this paper proposes a dynamic multi-stage focal loss function. The method introduces a novel convex–nonconvex progressive switching mechanism that adaptively reweights hard samples during training, thereby mitigating local optima and slow convergence. Additionally, it integrates eXplainable AI (XAI) techniques to enhance model interpretability and facilitate actionable insights into fraud patterns. Experimental evaluation on a real-world automobile insurance dataset demonstrates that the proposed approach consistently outperforms standard focal loss and multiple baseline methods: accuracy, precision, recall, F1-score, and AUC all show significant improvement. These results validate the method’s robustness, effectiveness, and practical utility for imbalanced fraud detection tasks.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In insurance fraud prediction, handling class imbalance remains a critical challenge. This paper presents a novel multistage focal loss function designed to enhance the performance of machine learning models in such imbalanced settings by helping to escape local minima and converge to a good solution. Building upon the foundation of the standard focal loss, our proposed approach introduces a dynamic, multi-stage convex and nonconvex mechanism that progressively adjusts the focus on hard-to-classify samples across training epochs. This strategic refinement facilitates more stable learning and improved discrimination between fraudulent and legitimate cases. Through extensive experimentation on a real-world insurance dataset, our method achieved better performance than the traditional focal loss, as measured by accuracy, precision, F1-score, recall and Area Under the Curve (AUC) metrics on the auto insurance dataset. These results demonstrate the efficacy of the multistage focal loss in boosting model robustness and predictive accuracy in highly skewed classification tasks, offering significant implications for fraud detection systems in the insurance industry. An explainable model is included to interpret the results.

Problem

• Mitigate class imbalance in auto insurance fraud detection
• Enhance machine learning model performance in imbalanced settings
• Improve discrimination between fraudulent and legitimate insurance cases

Innovation

• Dynamic multi-stage focal loss function
• Adjusts focus on hard samples progressively
• Explainable AI for fraud detection