Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection

TL;DR

This paper presents a novel federated learning and explainable AI approach for financial fraud detection, addressing data privacy, class imbalance, and transparency challenges to improve model performance and trust.

Contribution

It introduces a combined FL and XAI framework specifically designed for fraud detection, ensuring privacy, interpretability, and effectiveness in imbalanced data scenarios.

Findings

High performance metrics achieved with FL-based system

Enhanced transparency through explainable AI integration

Effective handling of data privacy and class imbalance

Abstract

Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for financial institutions, but some factors make the development of effective and efficient fraud detection systems a challenge. One of such factors is the fact that fraudulent transactions are rare and that many transaction datasets are imbalanced; that is, there are fewer significant samples of fraudulent transactions than legitimate ones. This data imbalance can affect the performance or reliability of the fraud detection model. Moreover, due to the data privacy laws that all financial institutions are subject to follow, sharing customer data to facilitate a higher-performing centralized model is impossible. Furthermore, the fraud detection technique should be transparent so that it does not affect the user experience. Hence, this research introduces a novel approach using Federated Learning (FL) and Explainable AI (XAI) to address these challenges. FL enables financial institutions to collaboratively train a model to detect fraudulent transactions without directly sharing customer data, thereby preserving data privacy and confidentiality. Meanwhile, the integration of XAI ensures that the predictions made by the model can be understood and interpreted by human experts, adding a layer of transparency and trust to the system. Experimental results, based on realistic transaction datasets, reveal that the FL-based fraud detection system consistently demonstrates high performance metrics. This study grounds FL's potential as an effective and privacy-preserving tool in the fight against fraud.