False positives are the scourge of the Money Laundering Reporting Officer (MLRO) responsible for protecting the reputation and security of a financial institution.  Every occurrence of a client record matching to a name on a sanction, risk or PEP register has to be investigated; the review and research of false positives costs institutions time and manual effort.  “Fuzzy” techniques are essential to find inexact matches, but they often produce large numbers of records for review and the vast majority of these will be false positives.

With some institutions swamped by the volume of false positives, the temptation to tighten match rules can be irresistible.  But whilst this might reduce the immediate pain of so many false positives, it often increases the probability of a more insidious risk, that of false negatives.  Whilst false positives cost time and effort, false negatives allow criminals access to the financial system and can result in fines for the
institution and the MLRO as well as a loss of commercial reputation.

Achieving a Balance

Financial institutions are instructed to take a risk-based approach to anti-money laundering (AML).  But the regulators have also shown that they are willing to flex their muscles if they judge that an MLRO is failing to take adequate steps to implement adequate AML procedures, including the accurate screening of clients.  No screening system can produce perfect results, so the challenge facing the MLRO is to implement a solution that produces minimal false positives without increasing the risk of missing genuine matches.

With simple matching approaches, there is a direct relationship between the number of false positives and the number of false negatives; decreasing one leads to an increase in the other.  Thankfully, there are ways of decreasing the number of false positives without increasing the risk of false negatives.

If you’d like a copy of this white paper, please register for a copy at the Datanomic Resource Library.