Combating Money Laundering: Effectiveness of Measures Taken by Sri Lankan Banks to Comply with Regulatory Frameworks

Abstract

Money laundering poses ongoing challenges for countries across the globe. The complexities associated with these challenges vary from one country to another, primarily due to factors such as the unique nature of their financial sectors, the specific anti-money laundering (AML) icies they have adopted, and the capabilities of their central banks in devising a comprehensive framework to address these issues effectively. To overcome these challenges and establish robust AML policies, each country must develop its framework through its central bank. This framework should aim to eliminate obstacles and enhance the effectiveness of the country's AML efforts. To accomplish this, careful identification and inclusion of relevant factors is essential. The paper aims to assess the effectiveness of measures taken by banks in Sri Lanka to comply with regulatory frameworks in combating money laundering. To do so mainly three areas are being considered such as commitment towards legislative compliances, adequate tools and devices, and employee training. To collect data a questionnaire was sent to banks of Sri Lanka and 117 responses were received the questions mainly targeted the executives and managers working at banks of Sri Lanka. SPSS statistics software was used for the data analysis part and a regression model was run considering the assumptions for to run regression model the results of the research showed that the banks of Sri Lanka are committed to legislative compliance and do have adequate tools and devices and do sufficient employee training. Apart from that research also identified that there are constraints that banks face when adapting technological advancements and international collaboration is not up to standards, so recommendations were provided such as investing in continuous technological advancements to stay competitive and aligned with global AML technological standards, AI and machine learning tools, such as natural language processing and predictive analytics, can vastly improve the effectiveness and efficiency of AML and Know Your Customer (KYC) programs by automating complex tasks, identifying patterns, and reducing false positives. The research focuses on assessing the effectiveness of measures in the banking sector in Sri Lanka, excluding non-banking financial institutions. It examines compliance requirements in employee training, legislative compliance commitment, and adequate tools. The study's scope may be limited by data availability, resources, and participant access.