Federated Learning for Privacy-Preserving Fraud Detection across Financial Institutions
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P138Keywords:
Federated Learning, Fraud Detection, Financial Institutions, Privacy-Preserving Machine Learning, Secure Aggregation, Differential Privacy, Financial Crime Analytics, Digital Banking Security, Collaborative Intelligence, Financial TechnologyAbstract
Financial fraud has become increasingly sophisticated as digital banking, online payments, fintech platforms, and cross-border financial services continue to expand. Conventional fraud detection systems are often limited by institutional data silos, where each bank or financial organisation trains models only on its own transaction records. This restricts the ability to detect coordinated fraud patterns that move across multiple institutions. However, direct sharing of customer transaction data raises serious privacy, regulatory, commercial, and security concerns. Federated learning offers a promising solution by allowing financial institutions to collaboratively train fraud detection models without transferring raw data outside their local environments. This article examines the use of federated learning for privacy-preserving fraud detection across financial institutions. It outlines a framework in which participating institutions train local models on private transaction data and share protected model updates for secure aggregation into a global fraud detection model. The study further considers privacy-enhancing mechanisms such as secure aggregation, differential privacy, encrypted model updates, and model auditing. It also discusses key challenges, including non-identically distributed data, communication overhead, model poisoning, inference attacks, regulatory compliance, and explainability. The article argues that federated learning can improve collaborative fraud intelligence while maintaining stronger data protection, provided that technical safeguards, governance structures, and human oversight are properly implemented. The study contributes to the growing discussion on privacy-preserving artificial intelligence in financial crime detection and digital banking security.
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