Individual cardholders are creatures of habit. Cardholders have "favorites"—or recurrences—over a wide variety of entities in their transaction streams. These entities might include favorite ATMs close to work or home, favorite gas stations along a daily commute, preferred grocery stores, and preferred online stores for shopping.
To improve fraud management, we’ve been developing analytics that identify these cardholder favorites. This new analytic technology helps distinguish between “in-pattern” normal customer spending and “out-of-pattern” suspicious transaction activity. This enables faster fraud detection at much lower false positive rates (declines on legitimate transactions).
How does it work? An advanced analytic algorithm maintains favorite lists within the card transaction profiles. These "Behavior Sorted Lists" are updated with each transaction so that the patterns of favorites evolve over time. The more frequent entries appear with greater recurrence and are ranked at the top of the list, while less frequent entries fall away and are replaced with new entries.
The graphic above compares performance of FICO® Falcon® Fraud Manager 6 models with and without the Behavior Sorted List technology. Falcon 6 models with Behavior Sorted Lists outperform the legacy Falcon models without it, looking at both fraud detection and false positive reduction. I’ve highlighted the relative improvement in fraud detection (“REL”) at a couple account false positive ratios (“AFPR,” or the number of accounts the model identified incorrectly as fraudulent for each actual fraud account found). Because of this boost in performance, Behavior Sorted Lists are being incorporated into all Falcon 6 consortium models released this year.
