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.Read More