Best practices for your investigation process

This page outlines best practices for using risk scores and explainability.

Using risk scores

Risk scores can be used in your investigation process to prioritize investigations of high-risk parties.

Common approaches include alerting based on investigator load or based on a chosen risk level:

Alert or investigate the top n parties in the output table based on risk score in descending order, depending on available investigator volume

Note: Investigator volume can change over time depending on assessment of the implied risk level (for example, a marginal conversion rate of lowest scoring alerted cases).
Alert or investigate all parties with a risk score above a certain threshold that is fixed month-to-month.

Note: Risk score threshold should be re-evaluated when using a new or retrained model, because the same threshold may correspond to a different risk level depending on the dataset or other aspects of the model.

Using explainability

The feature families with the highest positive attribution scores can be provided to investigators to direct their investigations, to decrease the time needed per investigation or increase success rate. For best results, consider what training or guidance your investigators need to handle investigations related to different feature families.

Post filtering and repeated alerts

AML AI risk scores identify high-risk parties, but do not separate out repeat alerts. You might want to apply post filtering to avoid re-alerting a party with a current or recent investigation without significant change in risk score or explainability.