Journal of Theoretical & Computational Science
Open Access

School of Computing, Glasgow Caledonian University, Glasgow Scotland, United Kingdom

Publications

• Mini Review   
  Anti-Money Laundering (AML) Detection Platform Leveraging Federated Learning with NVIDIA Federated Learning Application Runtime Environment (FLARE)
  Author(s): Venkatesh Upadrista*, Nitin Bhargava and Ram Gopal
  
  Money laundering remains a significant challenge to the global financial system, employing complex and evolving methods that outpace current regulations. Despite the implementation of Anti-Money Laundering (AML) compliance measures such as Know Your Customer (KYC) and Customer Due Diligence (CDD), sophisticated laundering schemes continue to exploit gaps in existing systems. Traditional rule-based monitoring systems often result in high false positive rates, leading to inefficiencies and increased operational costs. While machine learning has been employed to enhance anomaly detection, issues such as imbalanced datasets, frequent false alarms and limited adaptability to new money laundering tactics still persist. To effectively combat money laundering, there is a need for more advanced, collaborative solutions that can adapt to emerging threats. This research introduces an a.. View More»
  

  Abstract PDF