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Matching Multiple Factors

If your main focus is screening business transactions, you can skip to the next blog item you wanted to read – nothing to see here for you.

On the other hand, if you screen static data, like customer records or insurance policies, one way to cut down the number of matches is to attempt additional matches beyond the basic matching phrase. The results of these additional matches can then be used to better assess the quality of that match and potentially skip over the matches which do poorly on the additional comparisons.

It’s reasonable to assume that the additional matching is between elements of your data and elements in (or associated with) the watchlist listing. Typically, both of these records may have the following in common:

  • The address, city and country of residence or domicile
  • Date of birth
  • Identification number, like passport number, national id (like SSN or Cedula), corporate id (like TIN, DUNS id, or VAT id), SWIFT address, IMO number or call sign

In addition, a comparison of the full name from your data could be made to the full listed name.

There are pitfalls to many of these additional checks:

  • A company that has many locations may not be listed on the sanctions lists under all of them, so an address or even city comparison may fail when it’s valid
  • Multiple lists may list dates of birth in different formats, so comparisons of dates beyond year may be inaccurate unless the other components are known to be reliable
  • Some listed persons’ dates of birth are listed as approximate and should not be used in comparisons
  • Name comparisons will yield inaccurate results if the name field can contain multiple parties (e.g. joint account ownership)
  • Name comparisons, of course, must account for name variations (Bill vs. William), use of initials

So, how are these results used? Depends on your technology, of course. Basically, though, each comparison that is performed generates some sort of score. These scores can either be evaluated singly or can be combined into an overall weighted score. Systems can either display these values for informational and evaluational purposes, or may provide rules-based processing to ignore matches with low score values.

Categories: False Positive Reduction


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