Regularized Incremental Federated Learning for Fraud Detection in Vehicle Insurances

Federated Learning (FL) enables insurance companies to collaboratively train fraud detection models while maintaining data privacy. However, existing FL solutions are designed for static data and cannot support online training with continuous data streams from multiple sources. The main challenge lies in clients' (insurance companies) inability to automatically adapt to time-varying data flows in fraud detection tasks. To address this problem, we propose RegMeta-pFL, a personalized incremental FL scheme that combines Elastic Weight Consolidation (EWC) regularization and meta learning to effectively utilize incremental data from each client during training. Experiments on real-world insurance datasets show that RegMeta-pFL outperforms existing baselines, including FedAvg, FedProx, Per-FedAvg, and Fed_CIL, improving accuracy by 21.8%, 8.77%, 5.82%, and 2.71 % respectively, demonstrating its potential for fraud detection in the vehicle insurance sector.