The New York State Department of Financial Services (NYDFS) risk based banking rule, went into effect on January 1, 2017 and will have a significant impact on the validation of financial institutions’ transaction monitoring and sanctions filtering systems. The final rule requires regulated institutions to annually certify that they have taken the necessary steps to ensure compliance.

Data integrity is particularly interesting because it arguably hasn’t been given the same emphasis as other components of an effective anti-money laundering (AML) program, such as a risk assessment.

There has always been an interesting dynamic between the way compliance and technology departments interact with one another. This new rule will force institutions to trace the end-to-end flow of data into their compliance monitoring systems which could be a painful exercise. This exercise will demand the interaction between groups which may have stayed isolated in the past and it will require some parts of the organization to ask tough and uncomfortable questions to others. Clearly, gaps will be found and remediation projects will have to be launched to address those items.

As mentioned in my previous articles, traditional rule-based transaction monitoring systems (TMS) have architectural limitations which make them prone to false positives and false negatives:

Naive rules create a plague of false positives that are expensive for investigators to sift through
Sophisticated money launderers know how to circumvent rule-based systems leading to false negatives and potential fines from regulators

This article focuses on the third drawback of existing TMS solutions: how their inflexible data models lead to poor data quality, resulting in additional false positives and false negatives.

I think many of us working in the anti-money laundering (AML) technology space have experienced the frustration of spending many hours retrofitting new data types to squeeze into the rigid data model of a TMS. Unfortunately, the more effort we spend retrofitting data, the more likely we introduce data quality issues. Further, when we don’t complete it in a timely fashion, we’re exposed to risk of large fines from regulators. That said, there’s hope on the horizon from machine learning solutions that are more forgiving of disparate data formats.

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Data integrity can no longer be neglected in anti-money laundering (AML) programs

AML Data Quality: The Challenge of Fitting a Square Peg into a Round Hole

Data Derivatives

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