On 20. November at 2.15 pm Pavlo Tertychnyi will defend his doctoral thesis "Machine Learning Methods for Anti-Money Laundering Monitoring" for obtaining the degree of Doctor of Philosophy (Computer Science).
Prof. Marlon Dumas, University of Tartu.
Prof. Fethi Rabhi, The University of New South Wales (Australia);
Prof. Branka Hadji-Misheva, Bern University of Applied Sciences (Switzerland).
Money laundering (ML) poses a significant threat to global financial systems, enabling criminals to disguise the illicit origins of funds and integrate them into the legitimate economy. It not only has financial consequences but also undermines the stability of financial systems, threatens national security, and erodes public trust in financial institutions. Governments and law enforcement agencies worldwide are concerned about identifying ML activities. In turn, financial institutions deploy a variety of monitoring mechanisms to detect and report potential ML activities. These systems usually follow simple rules, but they have limitations in detecting complex and new ML schemes. Usage of machine learning algorithms – algorithms which use labelled historical data to form a decision – in the context of ML detection can significantly improve the effectiveness of the existing systems.
The goal of this thesis is to create a solution that combines different frameworks to automatically detect ML using machine learning. However, there are many challenges in developing such a solution. The increase in digital payments and global transactions has generated a huge amount of data to analyse. Different financial products make it even harder to detect ML because criminals can use various combinations of those products. Moreover, ML is a very rare event, which makes it difficult to develop effective machine learning models. Finally, ML schemes are constantly changing, requiring regular updates and adjustments of the approach.
The thesis makes four main contributions to this research area: (i) a framework that detects ML on individual customer level; (ii) a framework that detects ML on group customer level; (iii) a system that defines when to raise alerts for detected ML behaviour, which later are processed by specialized experts; (iv) a system that provides textual explanations on why those alerts were raised. These contributions together form a comprehensive solution for automated ML detection that meets important business requirements. The solution has been tested on real-life data with customer profiles, transaction histories, and input from anti-money laundering experts. The results were evaluated through computational experiments and feedback from domain experts.
The defence can also be followed in Zoom (Meeting ID: 983 8014 9390, Passcode: ati).