Developing an Integrated AML Risk Management Framework for Commercial Banks Based on Customer Risk Profiling and Enhanced Due Diligence

Abstract

This study examined the effectiveness of integrated Anti-Money Laundering (AML) risk management frameworks in commercial banks, focusing on customer risk profiling, technological adoption, and regulatory compliance mechanisms. Using both quantitative and comparative analysis, the research explored how transaction volume, source of funds transparency, geographical exposure, and politically exposed persons (PEPs) criteria influence risk categorization across low, medium, and high-risk groups. Results revealed that transparency in source of funds and monitoring of PEPs emerged as critical determinants in classifying customers into higher risk categories. Furthermore, the adoption of technology significantly enhanced AML efficiency, with automated transaction monitoring and data analytics dashboards demonstrating higher adoption rates and effectiveness scores compared to blockchain and machine learning tools. Overall, the findings suggested that an integrated approach combining advanced technology, risk-based profiling, and regulatory compliance improved detection accuracy, reduced fraudulent activity, and strengthened operational efficiency. However, gaps remained in addressing high-risk categories and ensuring the explainability of AI-driven tools for regulatory acceptance. The study emphasized the importance of balancing technological innovation with regulatory adaptability, particularly in cross-border contexts where financial crime risks are more complex. Future research should explore emerging technologies such as privacy-preserving AI and decentralized finance (DeFi) frameworks to strengthen global AML strategies while ensuring compliance and operational sustainability.