Building a Dynamic Segmentation Strategy for Effective AML Transaction Monitoring

In order to comply with the ever-changing anti-money laundering (AML) regulations and counter the financing of terrorism, financial institutions and iGaming companies must keep up with and adapt to the fast-paced world of technology and evolving money laundering techniques.

The issue, however, lies in the fact that most risk-based transaction monitoring solutions are outdated and not easily adaptable to new developments in the ways financial crime is committed. Typically, an AML transaction monitoring application has scenarios that monitor the customers and accounts that pose the most risk to the institution, yet this one-size-fits-all methodology is ineffective as customers transact differently. Which leads us to the question:

To segment or not to segment?

Many businesses spend countless time and money unnecessarily reviewing false positive alerts due to ineffective threshold tuning based on poor customer segmentation methodologies, which leads compliance officers to derive a threshold value of a given attribute based on the activity exhibited by the entire customer population. This ‘one-size-fits-all’ approach therefore results in high operational costs and can potentially cause actual suspicious activity to go undetected.

Significant effort is required to determine unique customer segments in order to implement an effective, data-driven customer segmentation strategy. This involves addressing a number of important considerations:

1. Targeted thresholds

Implementing a systematic customer segmentation methodology that is based on transaction activity and customer type rather than a customer’s combined transactions enables businesses to identify unique groups of customer behaviour and thus establish more targeted thresholds.

2. Focused Monitoring

Another effective AML transaction monitoring strategy and way of preventing the triggering of false positives is analysing customer segments and applying more focused monitoring. In order to do this, businesses must first combine groups of customer accounts that are deemed to be of higher AML risk and then monitor them using more conservative scenario threshold values. Once compliance officers have isolated high-risk groups, they should then separate said groups into similar segments, which, when using this process, are more accurate for grouping than traditional rule-based methods.

3. Continuous evaluation

Once this process is set-up, the model should be evaluated and updated regularly to establish the range of transactions, statistical distribution of segments, and help businesses stay up-to-date with changing customer behaviour, thus reducing the triggering of false positives.

When it comes to effective AML transaction monitoring, a dynamic segmentation strategy should be chosen over a one-size-fits all. It is therefore imperative that businesses provide their compliance officers with the right software to enable them in making the necessary measures to meet AML regulations.

AXON AML Transaction Monitoring enables you to easily adapt to ongoing regulatory changes

Increase automation: Minimise false positives by tailoring scenarios to customer or transaction risk and focusing on regulatory priorities.

Increase effectiveness over time: Tune rules through back testing without the need of technical personnel.

Give regulators and banking partners confidence: Make use of a ‘tried & tested’ system with a clear audit trail of monitoring and investigations.

Contact us at Computime Software for more information on how AXON can help your business comply with the ever-changing regulations.

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