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

Federated Learning protects customer privacy while improving fraud detection: The paper demonstrates that financial institutions can collaboratively train fraud detection models without directly sharing sensitive customer data, allowing them to build more effective systems while complying with data privacy laws.

Explainable AI makes fraud detection decisions understandable to humans: By integrating transparency tools into fraud detection systems, the research shows that AI predictions can be interpreted by human experts, which is essential for maintaining customer trust and enabling financial institutions to justify why transactions are flagged as fraudulent.

Class imbalance in fraud data is a significant technical challenge: The paper identifies that fraudulent transactions are rare compared to legitimate ones, creating imbalanced datasets that can reduce model reliability—a practical problem that responsible AI systems must address to avoid false positives that harm customer experience.

Combining privacy and transparency creates more trustworthy AI systems: Rather than treating data privacy and model interpretability as separate concerns, this research shows that using both Federated Learning and Explainable AI together creates fraud detection systems that are simultaneously protective of customer data and understandable to the people who use them.