Functional Features for Money Laundering Detection in a Retail Bank

Money laundering is a complex sequence of money transfers that traditional methods based on aggregated transactions struggle to detect. To improve upon this issue, the detection of such anomalies is framed as identifying unusual (co-) movements across dimensions in time and scale, with interactions between pairs of dimensions quantified using several projection-based methods.
Then, these multivariate functional interactions scores are embedded in unsupervised algorithms (isolation forest, KNN and LOF) generating an anomaly score.
Finally, this anomaly score ranks observations by their deviation from a functional baseline and allows the identification of suspicious transactions. Empirically, this methodology is applied to a large sample of bank transaction records, where customers' behaviors are represented as multivariate transaction curves across cash, wire, and international credit/debit dimensions, highlighting money laundering cases overlooked by conventional methods.
Evaluation against functional and tabular baselines confirms the effectiveness of the proposed methodology. In addition, the performance of the methodology is showcased in a series of realistic simulations.