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Fraudulent transactions and how to detect them remain a significant problemfor financial institutions around the world. The need for advanced frauddetection systems to safeguard assets and maintain customer trust is paramountfor financial institutions, but some factors make the development of effectiveand efficient fraud detection systems a challenge. One of such factors is thefact that fraudulent transactions are rare and that many transaction datasetsare imbalanced; that is, there are fewer significant samples of fraudulenttransactions than legitimate ones. This data imbalance can affect theperformance or reliability of the fraud detection model. Moreover, due to thedata privacy laws that all financial institutions are subject to follow,sharing customer data to facilitate a higher-performing centralized model isimpossible. Furthermore, the fraud detection technique should be transparent sothat it does not affect the user experience. Hence, this research introduces anovel approach using Federated Learning (FL) and Explainable AI (XAI) toaddress these challenges. FL enables financial institutions to collaborativelytrain a model to detect fraudulent transactions without directly sharingcustomer data, thereby preserving data privacy and confidentiality. Meanwhile,the integration of XAI ensures that the predictions made by the model can beunderstood and interpreted by human experts, adding a layer of transparency andtrust to the system. Experimental results, based on realistic transactiondatasets, reveal that the FL-based fraud detection system consistentlydemonstrates high performance metrics. This study grounds FL's potential as aneffective and privacy-preserving tool in the fight against fraud.

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