Indra Reddy Mallela1, Murali Mohana Krishna Dandu2, Raja Kumar Kolli3, Dr Satendra Pal Singh4, Punit Goel5
& Om Goel6
1Texas Tech University, Suryapet, Telangana, 508213, indrameb1@gmail.com
2Texas Tech University, San Jose, CA 95134, murali.dandu@walmart.com
3Wright State University, CO, 80104, USA, kolli.raja17@gmail.com
4Gurukul Kangri University, Haridwar, Uttarakhand , spsingh.gkv@gmail.com
5Maharaja Agrasen Himalayan Garhwal University, Uttarakhand, drkumarpunitgoel@gmail.com
6Abes Engineering College Ghaziabad, omgoeldec2@gmail.com
Abstract: Transaction monitoring plays a pivotal role in Anti-Money Laundering (AML) compliance, serving as a critical tool for detecting and preventing illicit financial activities. This paper examines the best practices and challenges associated with transaction monitoring models in AML compliance. Effective transaction monitoring systems leverage advanced technologies such as machine learning and artificial intelligence to analyze vast volumes of transaction data in real-time, identifying patterns indicative of money laundering. Best practices include adopting a risk-based approach to monitoring, ensuring continuous model validation, and integrating data from multiple sources for comprehensive analysis.
However, organizations face numerous challenges, including the complexity of regulatory requirements, the need for skilled personnel, and the potential for high false-positive rates that can strain resources. The evolving tactics of criminals also necessitate constant adaptation of monitoring models, making it imperative for financial institutions to remain agile in their compliance efforts. Moreover, balancing the privacy rights of customers with the need for thorough monitoring presents an additional layer of complexity. This paper aims to provide insights into optimizing transaction monitoring models while addressing these challenges, ultimately enhancing the effectiveness of AML compliance programs. By analyzing various case studies and regulatory frameworks, this research contributes to the ongoing discourse on best practices in transaction monitoring, offering recommendations for financial institutions striving to strengthen their AML strategies in an increasingly sophisticated financial landscape.
Keywords:
Transaction monitoring, AML compliance, money laundering detection, best practices, regulatory challenges, machine learning, artificial intelligence, risk-based approach, data integration, false positives, privacy rights, financial institutions.
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