False Alerts - 19 December 2011
The perennial complaint about watch-list filtering is that there are "too many false alerts". Before we can deal with this, we must define what we mean by a false alert and what the client means by "too many".

Unfortunately, the alert handlers only see the alerts, of which it is quite normal for 99% or more to be eventually identified as false. They forget completely the 97% or more customer names and transactions that the software passed automatically and which, without the software, they would have had to examine individually. So, for example, if the alert rate is a reasonable 3%, a screening of 10000 customers or transactions would be expected to generate 300 alerts. That means there are 9700 decisions that have been made without human intervention. If, of those 300, maybe 3 are expected to actually be the people on the list, this is still only 297 instead of 10000 that have required manual intervention to pass them. The first stage in managing the user expectation is to point out the number of decisions they don’t have to take at all, which is normally many more than those they have to take that turn our to be false.

The second part of managing false alerts needs, I believe, a new definition. This I call a "Valid Alert". A valid alert is one that we expect the software to generate.

Any string matching/search software has limitations, particularly when looking for “close” as well as exact matches. In fact, with name searches, even exact matches can turn out to be false, names are not unique identifiers. If there is a John Smith living in London, UK on the list and there is a John Smith living in London, UK who is a customer then, if the filter only checks Name, City and Country an alert will be raised against this customer, even if he is not the same John Smith. In this case, despite the alert being false, it is still valid that the software raised it. The diagram below illustrates the possibilities for watch-list filtering.
Alert representation
The extreme left represents names that are not on the reference list, the extreme right those who are. Between these extremes, there are three regions: The False and True alerts together consitiute what I call Valid alerts; those we want the software to generate. However, the boundaries of these regions are not well defined. The difference between a True and False alert can only be decided by reference to the Customer Due Diligence carried out on the customer to verify that they are a reputable person. It is both possible that a person with a name very close to that on a list, or even an exact match, is not the same person or that a person with a name which doesn’t appear to be a close match is in fact, the criminal against whom a sanction applies.

The border between an alert being raised or not is defined by the way the filter is configured. In some cases, for example domestic PEPs, it can be required that an alert is not raised even if the customer is the person on the list. The challenge is to agree with the business what is an acceptable Valid alert and so configure the filter to move the boundary between No Alert and False Alert as far to the right as possible without, of course, introducing a risk of missing a true alert.

The important point here is the agreement with the business. This is not a purely technical decision. Every business will have a different risk appetite and a different perception of the cost-benefit of employing alert handlers against risking missing true alert. A small private bank with 1000 high net-worth customers could probably accept a 10% alert rate – 100 alerts is not a huge number to deal with and the money laundering risk in private banking is high. A large retail bank with 10 million low risk customers would have to employ a whole department of alert handlers to deal with 1 million alerts and might never catch up a backlog, so they would be looking for a much smaller alert rate. The definition of what makes a valid alert is not fixed, but dependent on the perception of the business.