Anti-Money Laundering with Unreliable Labels
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2024
Författare
Bergquist, Jesper
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This report examines the effectiveness of Graph Neural Networks (GNNs) in detecting money laundering activities using transaction data with unreliable labels. It explores how weakly supervised learning, specifically with GNNs, manages the challenges posed by missing and inaccurate labels in anti-money laundering (AML) systems. The study utilizes simulated transaction datasets to compare the performance of GNNs against traditional statistical models. Findings indicate that GNNs, due to their ability to process relational data structures, demonstrate superior adaptability and accuracy in scenarios with label deficiencies. This research provides effective strategies for enhancing anti-money laundering systems by employing GNNs to more effectively manage data challenges.
Beskrivning
Ämne/nyckelord
GNN , AML , money laundering , machine learning , deep learning , graph neural networks