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FNA R&D: Improving AML/CFT/CDD Monitoring and Investigation

Background

In 2017 global banks were fined £5B for failures to detect and address money laundering. Current methods are insufficient in identifying money laundering, and costly in terms of large amounts of manual labour needed.

Method

Many suspicious patterns related to AML can be automatically detected with graph algorithms. Insightful visual graph dashboards help manual investigation and can prioritize cases more likely to be fraudulent. Visual dashboards allow case officers to overlay relevant different data sets, to filter out noise (irrelevant clusters, low risk accounts, etc.) and to recognize money laundering faster.

Benefits

Enhanced ranking of automatically detected AML patterns helps filter out noise allowing manual processing to focus on fewer and more highly qualified leads. This leads to more effective prioritization of KYC activities, saving time and money in AML follow-up activities.

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