Catch Me If You Can: Unmasking Anomalies in Financial Transaction Data : Exploring Self-Supervised Contrastive and Active Learning for Anomaly Detection in Label-Scarce Anti-Money Laundering

Sammanfattning

Money laundering is a global challenge that undermines financial systems and supports criminal activity, making effective anti-money laundering measures necessary. However, like most other realistic anomaly detection problems, challenges such as label scarcity, data contamination, imbalance, noise, etc. complicate the effective application of machine learning in anti-money laundering systems. Although different model architectures, data synthetisation, and active learning techniques have been tried, selfsupervised learning has recently shown promise. This thesis examines whether self-supervised learning can enhance anomaly detection in an anti-money laundering setting with applied active human-in-the-loop active learning feedback. To address this, the thesis compared the performance of an anomaly detection model (PReNet) trained on raw data against the same model trained on contrastive self-supervised embeddings of the same data. The thesis evaluates the performance of the PReNet model under two active learning strategies, based on uncertainty- and certainty based sampling over 9 iterations of updates. The results indicate that self-supervised embeddings did not enhance model performance relative to raw data on our dataset, contradicting previous literature. After 9 iterations, the raw baseline models performed substantially better than their embedded counterparts based on domain expert feedback. Future work could explore alternative self-supervised learning methods for tabular data, the use of different anomaly detection models, and broader datasets to assess how these techniques may be developed further.