Data key to Anti-Money Laundering efforts

A few weeks back I presented at the AML, Fraud and Financial Crime conference in Johannesburg.

Over 160 people attended – certainly one of the most well attended events I have been to in some years,

I think it is reasonable to assume that this interest was driven by the significant fines that have been meted our on numerous South African banks for breaching AML regulations.

Ultimately, many of the issues identified can be tied to Enterprise Information management failings – particularly in the areas of data governance, data quality and master data management. This was the central theme of my presentation, Avoiding Compliance Pitfalls, but was repeated as a concern by many of the other speakers.

I suggest that AML professionals need to take responsibility for the following:

  • Governing AML data. This means that AML teams must define their requirements for data to support AML, and must communicate and measure the ability of data to support anti money laundering models
  • Ensuring the quality of AML data. Linked to the data governance goals above, AML teams must define minimum data quality standards for AML data. Both Client Static and transactional data must be enhanced in order to ensure that AML policies and models are supported.
  • Building an understanding of the complete customer exposure and behaviour. “Single view of teh customer” means different things to different people. From an AML perspective it means being able to identify all transactions linked to a specific customer., so that suspect transactions and total exposure can be more accurately recognised.

Of course, data is not the only factor impacting AML. But as banks move towards risk based models for AML and fraud analytics, the role of quality data is becoming more and more a business issue.




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