Leveraging Federated Learning for Enhancing Anti-Fraud Systems in Fintech: Opportunities and Challenges

Federated learning (FL) is a revolutionary machine learning technology that protects data ownership while training unified AI models. By enabling multiple organisations to train machine learning models collaboratively by exchanging model updates instead of raw data, federated learning systems have great potential in areas where raw data are sensitive and cannot be easily shared, such as in financial technology (Fintech). Federated learning also emerges as a novel approach in the domain of anti-fraud systems to identify and combat economic crimes, such as fraud and money laundering, without sacrificing the security of sensitive financial information. This paper discusses recent developments using federated learning for Fintech and highlights its application in combatting fraud in Taiwan. Federated learning has successfully optimised fraud detection models across multiple financial institutions, as evidenced by key projects like the "Eagle Eye Fraud Detection Alliance Platform". Such initiatives prove that FL can significantly improve early fraud detection across institutions while ensuring data privacy through joint training of AI models. It also outlines a brief overview of security issues, Vision for Federated Learning, and the major challenges seen in widespread adoption, such as issues in model inversion attacks, data heterogeneity, and the robust encryption methods that can make it work. However, these problems do not outweigh the advantages of using federated learning to improve Fintech anti-fraud mechanisms. This paper then concludes with a discussion on possible future work and the usage of FL to also improve financial crime detection, presenting novel opportunities for institution-wise cooperation and a more effective anti-fraud scheme.