Enhancing Fraud Detection in Enterprise Resource Planning Systems through Federated Learning: A Comparative Study

Occupational fraud significantly impacts company finances and reputation. Enterprise Resource Planning (ERP) systems store extensive data that can be used for fraud detection, though legitimate transactions dominate the records, making fraud detection a challenging task. Companies should share findings on evolving fraud patterns to stay current and combat fraud collaboratively. However, data security and privacy concerns hinder the sharing of relevant ERP data and information on fraud incidents. Federated Learning (FL) offers a promising solution by enabling the exchange of model parameters instead of sensitive data. This study evaluates the effectiveness of FL for fraud detection in ERP systems compared to traditional machine learning methods. It considers scenarios involving companies not contributing fraud data, varying proportions of fraud cases, different fraud categories, and data volumes, and their influence on the FL process. Federated training, centralized training, and isolated training are compared, highlighting the potential of FL for enhancing occupational fraud detection in ERP systems.