Enhancing Global Banking Security: A Novel Approach Integrating Federated Learning and CNN-GRU for Effective Anti-Money Laundering Measures

Abstract

Present-day banking sector analysis shows the growth of concern on AML in the face of sophisticated laundering practices by financial houses. Traditional means of detection in such cases seem to be out of the loop because of voluminous data in the system and dynamic nature of financial transactions. This research intends to boost global banking security through the implementation of Federated Learning (FL) with Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for an enhanced AML system. It caters to the necessity of privacy-preserving solutions, increasing the detection accuracy. This proposed method relies on FL so that sensitive financial data is decentralized; hence, the institutions are capable of collaborative learning of patterns in fraudulent activities without infringing upon their privacy. This has allowed the model to significantly improve anomalous transaction patterns detection, which was previously quite difficult due to the inability of CNNs in feature extraction to capture temporal dependencies. 98.7% of accuracy gains from this combination are very promising since hybrid models outperform conventional AML systems in efficiently processing large and complex datasets while eliminating false positives. This study shows how the innovative integration of FL, CNN, and GRU improves not only the detection capabilities but also ensures that stringent privacy regulations are met. Thus, the proposed method offers a scalable and effective solution for the global banking sector. It also surpasses traditional techniques in terms of security and efficiency in the battle against money laundering in the emerging financial scenario.