Federated Learning and Privacy-Preserving AI for Cross Institutional Financial Fraud Detection

Gopinathan Rathinavelu

Federated Learning and Privacy-Preserving AI for Cross Institutional Financial Fraud Detection

Authors

Gopinathan Rathinavelu

Keywords:

Federated Learning, Financial Fraud Detection, Privacy-Preserving Machine Learning, Secure Aggregation, Differential Privacy, Collaborative Financial Intelligence

Abstract

Financial fraud increasingly operates across institutional boundaries, while conventional frauddetection systems remain constrained by fragmented datasets, privacy requirements, datasovereignty obligations, and competitive barriers to information sharing. This paper develops a privacy-preserving federated learning framework

References

1. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54, pp. 1273–1282, 2017.

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Published

2023-11-06

How to Cite

Gopinathan Rathinavelu. (2023). Federated Learning and Privacy-Preserving AI for Cross Institutional Financial Fraud Detection . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 3239–3263. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5850