A Hybrid Federated Learning Model for Insurance Fraud Detection
Y. Supriya, Nancy Victor, Gautam Srivastava, Thippa Reddy Gadekallu · 2023
Mission-critical systems are significant for the survival of any organization. Financial systems, communication systems, and electricity grid systems are some examples of mission-critical applications. Any damage to such systems can cause business operations to fail. Insurance fraud is one of the challenges faced by mission-critical systems today. Insurance fraud detection has historically been a manual task left to claim agents, who look over the evidence and draw an inference based on their intuitions. Recently, machine learning (ML) has been predominantly used to automate the process of insurance fraud detection. Despite the success of ML models in detecting insurance fraud, privacy preservation of insured personal data continues to be a problem. To address this issue, we present a novel hybrid approach. The proposed work suggests an automated way to control the process of vehicle insurance claim fraud detection in the insurance industry. The proposed study combines Federated Learning (FL), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to incorporate the advantages of each technology. The suggested model uses GA for optimal feature subset extraction. The optimized feature subset is then fed into a Federated learning with a Particle swarm optimization (FPSO) model. The findings show that the suggested hybrid model has an accuracy of 94.47% and that it may be improved even further by using other nature-inspired algorithms that are only used for fraud detection.