Data Quality, Entity Resolution, and OFAC Compliance
By Robert Barker, Infoglide Senior Vice President & Chief Marketing Officer
In a February post blogger Steve Sarsfield talked about government mandates that direct financial institutions to avoid doing business with known “bad guys”:
The mandates have to do with the lists of terrorists offered by the European Union, Australia, Canada and the United States. For example, in the U.S., the US Treasury Department publishes a list of terrorists and narcotics traffickers. These individuals and companies are called “Specially Designated Nationals” or “SDNs.” Their assets are blocked and companies in the U.S. are discouraged from dealing with them by the Office of Foreign Asset Control (OFAC)… If your company fails to identify and block a bad guy… there could be real world consequences such as an enforcement action against your bank or company, and negative publicity.
He goes on to describe the role that data quality software plays in addressing the problem. While I agree with Steve that improving data quality is an important component of some solutions, I’d emphasize that it’s critical to know when and where to improve it. Too much “quality” can actually hurt a solution’s effectiveness.
Professor John Talburt (ERIQ) illustrated this notion in a recent guest post. Making the case for using entity resolution to find hidden relationships, he first showed how the absence of sufficient attributes can cause false positives. He then went on to say:
Even given that the set of identity attributes is large enough to avoid a false positive, the larger problem with matching as a surrogate for entity resolution is that it produces false negatives. For example, “Mary Doe, 234 Elm St” and “Mary Smith, 456 Pine St” do not match, but does that mean they are not references to the same entity? It could very well be the case that Mary Doe married John Smith and moved to his house at 456 Pine St.
So in looking for bad actors, suppose the address of one of the two Mary Does above had been resolved to the “correct” address by applying data quality software before using entity resolution to search for hidden relationships. We might have never discovered that Mary Doe married the nefarious John Smith who is on the OFAC list!
If the goal of the solution is merely compliance with a minimum of false positives, data quality can help achieve these goals. But if the goal of the solution is to find bad guys by discovering non-obvious relationships, false negatives are a more important consideration. While false positives are a costly annoyance that require extra resources to resolve, false negatives can mean missing bad guys altogether, and that hurts much more than the bottom line. It can mean not complying with the mandates.
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