Federated Learning and Explainable AI for Decentralized Fraud Detection in Financial Systems

Abstract

The dynamic changing nature of fraud patterns and necessity to safeguard sensitive customer data make it difficult for financial institutions to detect fraudulent activities. We propose a novel approach to decentralized financial system fraud detection by merging Federated Learning (FL) with Explainable AI (XAI). Since no two financial institutions share raw data, the proposed system is able to train a unified, effective fraud detection model without compromising data privacy or regulatory norms due to FL. We have integrated Explainable AI (XAI) techniques to make the model transparent for stakeholders so they can interpret the result and trust the decision process of the system. Compared to traditional centralized methods, the proposed approach can achieve better detection accuracy with less false positives and better interpretability based on experimental results. We believe our results would lead to a fruitful way to adopt FL along with XAI mechanisms which will ensure insight provision for fraud detection without breaking down the privacy and secrecy of the underlying data and improving the overall transparency and accountability of financial system.