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.