Financial Fraud Detection Using Explainable AI and Federated Learning

Financial fraud is a growing concern that threatens the integrity of financial institutions and customer trust. Traditional fraud detection methods, which rely on rule-based systems and centralized machine learning models, often struggle to keep up with evolving fraudulent tactics. Additionally, the black-box nature of many machine learning models limits their interpretability, making it difficult for financial analysts and regulatory bodies to trust and validate fraud detection outcomes. To address these challenges, Explainable AI (XAI) enhances model transparency by providing human-understandable explanations for fraud predictions, while Federated Learning (FL) enables privacy-preserving, collaborative model training across multiple institutions without sharing sensitive data. Federated Learning offers a decentralized approach that allows financial institutions to train fraud detection models on diverse, distributed datasets while ensuring compliance with data protection regulations. This improves model generalization and robustness by leveraging insights from various sources without compromising customer privacy. At the same time, XAI ensures that these models remain interpretable, helping analysts understand the reasoning behind fraud alerts, identify potential biases, and refine detection strategies accordingly. The combination of XAI and FL enables institutions to strengthen fraud detection capabilities while adhering to ethical AI practices and regulatory requirements. The integration of Explainable AI and Federated Learning in financial fraud detection offers a promising solution to the challenges of transparency and privacy. XAI improves the interpretability of fraud detection models, making them more accountable and understandable for stakeholders, while FL facilitates secure and efficient model training across different organizations. This paper explores the synergy between these technologies, discussing their advantages, challenges, and potential applications in enhancing fraud detection. The Combination of Federated Learning (FL) and Explainable AI (XAI) delivers a powerful solution for financial fraud detection offering strong privacy guarantees, improved model performance and enhanced transparency. This approach supports regulatory compliance and fosters confidence among stakeholders in the deployment of AI driven fraud prevention.