Amaretto: An Active Learning Framework for Money Laundering Detection

Monitoring financial transactions is a critical Anti-Money Laundering (AML) obligation for financial institutions. In recent years, machine learning-based transaction monitoring systems have successfully complemented traditional rule-based systems to reduce the high number of false positives and the effort needed to review all the alerts manually. Unfortunately, machine learning-based solutions also have disadvantages: while unsupervised models can detect novel anomalous patterns, they are usually characterized by a high number of false alarms; supervised models, instead, usually offers a higher detection rate but require a large amount of labeled data to achieve such performance. In this paper, we present Amaretto, an active learning framework for money laundering detection that combines unsupervised and supervised learning techniques to support the transaction monitoring processes by improving the detection performance and reducing the compliance management costs. Amaretto exploits novel selection strategies to target a subset of transactions for investigation, making more efficient use of the feedback provided by the analyst. We perform the experimental evaluation on a synthetic dataset provided by the industrial partner, which simulates the profiles of clients trading in international capital markets. We show that Amaretto outperforms state-of-the-art solutions by reducing money laundering risk and improving detection performance. In particular, we compare state-of-the-art unsupervised and supervised techniques commonly used in the AML domain with the ones implemented in this work. We show that the Isolation and Random Forests of Amaretto perform best in the task under analysis, with an AUROC of 0.9 for the first (20% better on average) and a detection rate of 0.793 for the second (30 % better on average). In addition, they are characterized by lower investigation costs computed in terms of the daily number of transactions to be examined and the number of false positives and false negatives. Finally, we compare Amaretto against a state-of-the-art active learning fraud detection system, achieving better detection performances and lower costs in all the analyzed scenarios. Worth mentioning, Amaretto improves the detection rate up to 50 % and reduces the overall cost by 20% in the most realistic scenario under analysis.

Automated summary

This paper presents Amaretto, an active learning framework for money laundering detection that combines unsupervised and supervised learning techniques. Experimental evaluation shows that Amaretto outperforms state-of-the-art solutions by reducing money laundering risk and improving detection performance, while also reducing investigation costs.