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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.Item Intelligent money laundering detection approaches in banking and E-wallets: a comprehensive survey(Springer, 2025) Kadamathikuttiyil Karthikeyan, G.; Bhowmik, B.The rapid evolution of financial technologies (FinTech) has propelled the world into a more dynamic and sophisticated digital financial landscape. This transformation has significantly expanded financial inclusion, offering new opportunities to individuals who were previously excluded from or had limited access to traditional banking services. Financial inclusion is crucial as it provides access to a broad spectrum of financial services, including bank accounts, credit and debit facilities, and e-wallets. While the rise in digital transactions has been driven by cost efficiency, convenience, and enhanced security measures, it has also led to an increase in economic crimes, particularly money laundering, resulting in substantial global economic losses. Consequently, the need for effective strategies to combat money laundering has never been more pressing. This study thoroughly investigates the state-of-the-art techniques in money laundering detection harnessing the capabilities of artificial intelligence (AI) technologies. First, we provide an overview of economic crimes and classify their various types, setting the stage for a focused discussion on money laundering. The paper then explores the money laundering landscape, including its impact and recent trends, followed by a discussion on different prevention and detection strategies. The paper also delves into AI-driven detection strategies, particularly those targeting money laundering, including the detection of laundering activities through e-wallets. Additionally, we address the research challenges associated with money laundering detection, such as the issue of class imbalance in financial datasets, and propose solutions to overcome it. Finally, the paper provides insights into future directions for research, aiming to equip the research community with the tools necessary to formulate proactive strategies for preventing and mitigating money laundering and related economic crimes. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.