A Federated Learning Model for the Detection of Insurance Claims Fraud

Abstract

Practical insurance fraud detection solutions require sufficient quality data from insurers to build effective models. However, insurance data is generally proprietary information for specific insurance companies and thus not publicly available. Also, the Insurance datasets are often imbalanced, making it challenging to develop fraud detection models that are not biased. Data privacy and class imbalance are two significant challenges when developing artificial intelligence applications in the insurance setup. In this research study, we tackle these challenges and propose a decentralized and privacy-preserving federated approach using an adjusted random forest model. The method is asynchronous federated learning of the traditional adjusted random forest classifier, i.e., achieving a higher performance and accuracy level than the traditional centralized learning approach. Based on it, we achieved secure collaborative machine learning that allows the training of quality federated fraud detection models from imbalanced data without sharing data. Experiments on Kaggle and Oracle insurance datasets demonstrate that the federated adjusted random forest classifier is more accurate and efficient than the non-federated counterpart. Our model is verified to be practical, efficient and scalable for real-life insurance fraud detection tasks.