Faculty Publications
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Item Money Laundering Detection in Banking Transactions using RNNs and Hybrid Ensemble(Institute of Electrical and Electronics Engineers Inc., 2024) Girish, K.K.; Bhowmik, B.The financial sector has witnessed significant transformations due to the emergence of financial technology (FinTech), transitioning from traditional paperbased processes to a dynamic digital ecosystem. Despite the industry's advancements driven by FinTech innovations, concerns persist, particularly regarding financial fraud, notably money laundering. Perpetrators exploit modern technologies to launder illicitly obtained funds, posing a global threat to economies. Effective detection mechanisms for money laundering are crucial. This paper introduces a novel approach utilizing a recurrent neural network (RNN) for detecting money laundering in banking transactions. The proposed framework exercises standalone RNN models such as LSTM, GRU, BiLSTM, and stacked RNN models for the detection. Additionally, the effectiveness of hybrid ensemble models combining RNNs with XGBoosts is investigated. The evaluation achieves standard performance metrics, with the stacked RNN model achieving 92% accuracy. Surpassing it, the ensemble model achieves an impressive 95%. These results underscore the superiority of hybrid ensemble models over standalone RNNs, particularly in accurately detecting money laundering activities. © 2024 IEEE.Item Enhancing Money Laundering Detection in Bank Transactions using GAGAN: A Graph-Adapted Generative Adversarial Network Approach(Springer Science and Business Media Deutschland GmbH, 2025) Kadamathikuttiyil Karthikeyan, G.; Bhowmik, B.The past decade has witnessed profound transformations in the financial sector, driven by the integration of cutting-edge technologies into its core operations. Consequently, banks are increasingly utilizing technologies such as artificial intelligence (AI), blockchain, and big data analytics to offer personalized services, streamline transactions, and improve risk management, enabling the development of new financial products and services that cater to the diverse and evolving needs of customers. Despite these benefits, the banking landscape has also brought about complex challenges, particularly in the fight against money laundering. Money laundering remains a significant threat to the integrity of financial systems, as criminals exploit digital advancements to conceal illicit activities. The growing complexity of digital transactions and the increasing volume of financial data have made detecting and preventing money laundering more challenging than ever. Existing AI-based solutions, while effective to some extent, often grapple with class imbalance issues. This paper addresses the challenge by introducing a novel model named GAGAN (Graph Attention Generative Adversarial Network) and enhances the detection of money laundering activities in bank transactions. The proposed model further addresses the issue of class imbalance, by incorporating Conditional Generative Adversarial Network (cGAN) and Graph Attention Networks (GAT). The GAT classifier is then employed to accurately classify transactions, leveraging attention mechanisms to focus on the most relevant parts of the graph. Empirical results reveal that the proposed model achieves impressive performance metrics, with an accuracy of 98.62%, precision of 98.10%, recall of 98.92%, F1 score of 98.49%, AUC-ROC of 0.99, and a MCC score of 0.991. These results underscore the efficacy of the model in accurately identifying money laundering transactions, showcasing its potential as a robust tool for financial crime detection. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.