Background

Created in 1976 in the United States, and in France since 1989, the fight against money laundering (Anti Money Laundering or AML) is at the heart of the challenges facing financial institutions.
The 2008 financial crisis highlighted the need to combat tax havens whose opacity allows actors to prosper and carry out transactions that threaten the stability of the financial system, with a much less stringent framework of prudential rules and supervision.

Challenges

One of the main issues in AML is the reduction of false positives, i. e. false alarms, which are issued by current control systems. Indeed, false positives represent more than 98% of all alerts. This is where artificial intelligence finds its usefulness, in order to reduce this percentage and thus allocate more resources for real cases of fraud.
In addition, its use in KYC procedures increases customer knowledge and therefore improves safety.

 

 

Solutions

Identifying redundant data through semantic analysis or statistical analysis of files containing customer information and transactions can reduce false positives.
The machine learning allows to detect transactional and behavioural anomalies by analysing banking movements.
The analysis of external and unstructured data can also provide valuable information for the fight against money laundering.

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