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 rulebased 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.

Transaction Monitoring is one of the main labor-intensive tasks of anti-financial crime and it requires to scrutinise billions of transactions per month against possible crimes. The first step in the process is the correct identification of the involved parties. This foundational step defines the focal entities on which transaction monitoring algorithms rely to spot suspicious events. Unfortunately, the loose syntax of protocols and the free text fields of inter-banking communications make party disambiguation particularly challenging. The first step of a fully automated data-driven strategy is thus the detection of the actual entity owning or using a given account.In this paper, we leverage data-driven techniques to identify and disambiguate the owners of accounts involved in cross-border international transactions when a Financial Institution only knows a minority fraction of such parties as its own customers. For this, we propose a data science pipeline relying on hierarchical clustering to capture similarities among names of parties involved in actual transactions. We test and tune the proposed approach using a large, real-world, multi-language, proprietary dataset of actual international transactions. Our highly parallel implementation completes the identification of parties that share an account and identifies all accounts owned by a party with f-score higher than 0.8.