MAD: Multicriteria Anomaly Detection of Suspicious Financial Accounts from Billions of Cash Transactions

This paper presents a real-world deployment case study on using unsupervised anomaly detection for Anti-Money Laundering (AML). Using more than 2 billion anonymized bank transactions that Intesa Sanpaolo, a primary Italian financial institution, registered over 8 months, we developed, tuned and deployed a machine learning pipeline in production. Experts from Intesa Sanpaolo validated the performance of our approach against the institution's traditional rule-based system and checked new real-world cases the system allowed them to identify. Besides increasing both precision and recall by a factor of 6 in the detection of high-risk cases, our pipeline raises 200+ additional alerts during the 8-month period, manually identified by branch managers, but missed by the rule-based system. More importantly, a manual inspection of 100 new unseen cases revealed 28 significant previously unreported cases. The pipeline, now fully deployed in Intesa Sanpaolo's Transaction Monitoring system, highlights the advantages of machine learning over traditional approaches typically adopted in this traditionally very conservative sector.