THE FUTURE OF ENTITY DUE DILIGENCE

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THE FUTURE OF ENTITY DUE DILIGENCE

The world has gone through an incredible amount of technological transformation over the past ten years.  While it may seem hard to imagine that change will continue at this pace, it’s not only likely to continue, but it will accelerate. There are various functional areas within institutions that support global commerce, but some have been laggards in adopting new technology for a plethora of reasons.

Structural market trends will force organizations to innovate or they will be subject to consolidation, reduction of market share, and, in some circumstances, complete liquidation.  Future proofing the entity due diligence process is one key functional area that should be part of an organization's overall innovation road map because of the impacts of trends such as: rising regulatory expectations, disruptive deregulation initiatives, emergence of novel risks, explosion of data, quantifiable successes in artificial intelligence (AI), and changing consumer expectations.

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

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

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:

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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