Machine Learning for money laundering detection

This thesis explores the application of machine learning techniques for the detection of money laundering activities, a persistent challenge in the financial sector that undermines the integrity of economic systems worldwide. The study begins with a comprehensive introduction to money laundering, outlining its processes, the difficulties in traditional detection methods, and the significant potential of machine learning as a transformative approach. A thorough literature review reveals the landscape of existing research on money laundering detection, highlighting the limitations of conventional methods and the emergence of data-driven strategies. Key machine learning algorithms, including supervised and unsupervised learning techniques, are examined for their applicability to fraud detection, providing a theoretical framework for subsequent analysis. The methodology chapter details a structured approach to research design, including the selection of data sources, ethical considerations, and the implementation of machine learning algorithms. The practical application of these techniques is demonstrated through an empirical study, where various models are trained and evaluated using real-world datasets, illustrating the efficacy of machine learning in identifying suspicious transactions. Results indicate that machine learning models significantly outperform traditional methods, offering improved accuracy and efficiency in detecting potential money laundering activities. The discussion reflects on the implications of these findings for financial institutions and regulatory bodies, addressing the limitations of the study and proposing avenues for future research. Ultimately, this thesis contributes to the understanding of how advanced analytical techniques can enhance the detection and prevention of money laundering, providing recommendations for practitioners and policymakers to strengthen compliance and safeguard the financial ecosystem.