Enhancing Anti-Money Laundering Protocols: Employing Machine Learning to Minimise False Positives and Improve Operational Cost Efficiency

Recent advancements in anti-money laundering (AML) strategies underscore the imperative for more accurate and efficient detection systems. This paper delineates a robust approach utilising machine learning (ML) and artificial intelligence (AI) to refine AML frameworks within national financial institutions, reducing false positive alerts. Integrating data from ‘Know Your Customer’ (KYC) initiatives with comprehensive transactional datasets, our methodology identifies high-risk transactions and complies with rigorous regulatory standards while curtailing operational costs. Through a comparative analysis of various ML models—including Decision Trees, Random Forest, Support Vector Machines, Logistic Regression, Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) —we have identified that the Random Forest model notably decreased false positive rates to 2.1% and maintained an elevated detection rate for potentially illicit transactions. This model markedly surpasses traditional rule-based systems in performance, confirming its efficacy and suitability for broad implementation. By streamlining AML processes and diminishing compliance-related expenditures, this study presents a scalable and efficient model for financial institutions, optimising operational efficiency and fostering better cost-effectiveness in combating economic crimes. This analysis identifies the most effective models that balance detection accuracy with the need for explainability, an essential requirement for gaining trust from regulatory bodies and internal compliance teams. The findings of this study contribute to the field by offering a replicable and efficient AML framework that can safeguard financial institutions against regulatory penalties and reputational risks.