Title: Innovations and Discoveries in the Multidisciplinary Research
Chief Editor: Dr. Padmavathi S. M.
Associate Editor: Dr. Poonam Sachin Kadlag
Co-Editor: Dr. Shailaja A Akkur
ISBN: 978-81-69857-36-9
Chapter: 1
DOI: https://doi.org/10.59646/785/1
Authors: S. Yash Sriram, R. Raam Harish, and Dr. A. Shameem
Abstract
Money laundering has continued to be a major challenge facing financial services around the world. The increasing sophistication of financial crimes and the continued rapid growth of digital banking, cryptocurrency transactions, and cross-border financial transactions has resulted in the existing Anti-Money Laundering (AML) systems becoming less and less effective in identifying or preventing illegal financial activities. Most of the current AML systems have been developed as rule-based monitoring systems that rely heavily on manual investigations which lead to a considerable number of false positive results and require significant human interaction for processing. The emergence of Artificial Intelligence (AI) as a new technology provides an opportunity to revolutionize the AML processes in terms of data analytics, predictive modelling, anomaly detection, and real-time monitoring of potential money laundering activities. The purpose of this conceptual paper is to identify the opportunities that AI presents for improving AML systems of financial institutions, based on current literature, regulatory frameworks, and technological advancements. Specifically, the paper investigates existing AI-based systems used to detect and prevent money laundering activities. The paper explores a range of AI technologies including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and predictive analytics and examines how these technologies can be applied within the context of transaction monitoring, customer due diligence, suspicious activity detection, and risk assessments. Artificial Intelligence (AI) is a powerful tool that can revolutionize the Anti-Money Laundering (AML) framework within financial institutions, helping them to fight the financial crime risk. The benefits of adopting AI into AML programs include the accuracy of detecting criminal activity; efficiencies linked to conducting investigations; reducing costs associated with investigating potential money laundering activity; developing customer profiles to assess risk; and preventing financial crimes proactively. The study finds AI has potential benefits associated with detecting various forms of financial crime; however, it has also identified issues that need to be addressed including data privacy, algorithmic bias, explainability, regulatory compliance, and cybersecurity. From a theoretical perspective the benefits of using AI in AML are many and therefore economic benefits will accrue to those who adopt AI in their AML programs. The authors also provide recommendations to assist policymakers, regulators, and financial sector stakeholders enhance the effectiveness of AI-powered AML programs.
Keywords: Artificial Intelligence; Anti-Money Laundering; Financial Crime; Machine Learning; Compliance Management; Fraud Detection; and Regulatory Technology.