Conference Papers
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Item Recent Advancements and Challenges in FinTech(Institute of Electrical and Electronics Engineers Inc., 2023) Girish, K.K.; Bhowmik, B.The rapid advancement of technology in recent years has brought about numerous changes in various industries, and the financial sector is no exception. The rise of financial technology (FinTech) has disrupted traditional banking and financial services by offering more convenient, accessible, and personalized services to customers. Contrarily, financial services have become more efficient, cost-effective, and secure with FinTech, enabling people to manage their finances with just a few clicks, even on their smartphones. FinTech has also created new opportunities for financial inclusion, making it possible for people who were previously unbanked or underbanked to access financial services. Despite its many benefits, the rise of FinTech has also brought about several challenges. This paper gives an overview of FinTech, its progress, and its importance. Following this, significant challenges of FinTech are highlighted to ensure its long-term success and continued growth. The recent literature shows the way how it is transforming our perceptions. © 2023 IEEE.Item Money Laundering Detection in Imbalanced E-wallet Transactions with Threshold Optimization(Institute of Electrical and Electronics Engineers Inc., 2024) Doddamani, S.S.; Girish, K.K.; Bhowmik, B.The swift embrace of technology within the financial industry has driven the extensive utilization of electronic payment systems, providing smooth money transfers and substituting outdated paper-based procedures. Consequently, mobile payment systems centered on electronic wallets (e-wallets) have signifi-cantly transformed contemporary finance, introducing improved security measures and transaction functionalities. However, the prevalence of unlawful activities, including money laundering and associated fraud via e-wallets, presents a substantial threat to the integrity of the financial sector. This research paper delves into the pivotal role of machine learning models in identifying money laundering activities within e-wallet transactions. The study focuses on addressing the imbalance inherent in the PaySim dataset through the oversampling technique. Employing three distinct models - Logistic Regression (LR), Gradient Boosting, and XGBoost - the research systematically evaluates their effectiveness. Notably, XGBoost emerged as the standout performer, showcasing exceptional accuracy at 99.88%, precision at 0.9984, and sensitivity at 0.999. Furthermore, a threshold moving technique is employed to enhance the model's efficiency, optimizing its performance in detecting potential instances of money laundering within e-wallet transactions. © 2024 IEEE.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.