Key challenges Financial Institutions face when tuning AML Transaction Monitoring software


Financial institutions across the globe are under unprecedented regulatory pressure to improve their AML Transaction Monitoring systems and ensure they are correctly tuned to detect money laundering and terrorist financing. Recent fines given to institutions who failed to detect suspicious activity and therefore meet regulatory standards have served to demonstrate the importance of having an effective compliance program and the right rule-tuning in place.

Finding the right balance in rule-tuning

Getting the tuning right can be challenging without the appropriate configuration. Solutions can either generate too many false positives, or the scenarios can be “turned down” so that genuine suspicious activity goes undetected. While triggering excessive false positive alerts costs both time and resources – as compliance teams struggle to handle the high number of alerts in depth – doing the opposite and performing tuning to reduce the amount of false positives can lead to officers overlooking suspicious activity, thus undermining the efficacy of the compliance program and potentially subjecting the company to regulatory scrutiny and eventual penalties.

The various AML Transaction Monitoring rules 

Rules can vary in complexity depending on the monitoring needs of the institution in question, and are designed to monitor customer activity outside the scope and ability of profile-based monitoring. Here are a few of the many rules that can provide the tools necessary to fine-tune an AML Transaction Monitoring solution, as well as the challenges they may pose.

In an attempt to reduce false positives, financial institutions often create exclusion lists. These are lists of customers they’ve marked as “non-suspicious” and therefore excluded from the transaction monitoring rules based on their historical alerts which were ultimately flagged as false positives. While this approach certainly does its job in reducing false positives, these lists must be reviewed regularly for potential changes in their transactional behaviour to prevent illegitimate transactions getting through undetected and unimpeded.

The objective of the structuring rule is to identify attempts to avoid regulatory reporting of a large cash transaction by breaking the transaction into smaller amounts that fall below the reporting threshold. However, a legitimate businessperson may make a number of deposits in amounts that look similar to a structuring pattern, in which case, the rules needs to be tuned to trigger an alert based on a combination of the pattern of the transactions and the profile of the customer, rather than the transaction activity alone.

The aim of applying a velocity rule is to identify suspicious activity and flag accounts where there is rapid movement of funds into and out of an account, such as a large part of a deposit which is debited from an account in a short period of time. However, if a customer instantly pays off a high volume of bills upon receiving their monthly pay, this could trigger an alert because of the velocity of the transactions. Therefore, such legitimate behaviour patterns need to be taken into account when tuning the software in order to reduce triggering false positives.

A final challenge of tuning rules is that it is not a one-time task. Rules need to be continuously applied and reviewed over time to identify potential new risks that are not covered by the current monitoring process in place. 

Ensure effective AML Transaction Monitoring with ComplyRadar

ComplyRadar minimises false positives by tailoring scenarios to customer or transaction risk. It enables you to increase effectiveness over time by fine tuning rules through back testing without the need of technical personnel. Contact us for more information on how we can help you give regulators and banking partners confidence with a clear audit trail of monitoring and investigations.

Share this article