Tech Time: Keep pace with evolving fraud detection
To evaluate the effectiveness of fraud models, auditors need to test the underlying rules.
Assessments of the effectiveness of fraud detection models need to keep pace with the maturation of the algorithms powering those tools, suggests Chris Nickerson, CEO of Lares, a business security consulting firm based in Denver.
At the foundation of rules built into payment fraud detection is the identification of anomalies, such as a sudden jump in spending or geographical trending, which flags transactions that happen outside the region the cardholder typically frequents. Over time, that latter algorithm has become more sophisticated to differentiate between online purchases and “land speed violations” where, for example, one in-person transaction happens in Denver and another in Louisville, Kentucky, 30 minutes later.
Machine learning and AI are also working their way into fraud detection systems, facilitating analysis of the different facets of a transaction, including payment system, monetary systems, location and cardholder profiler, Nickerson explains.
“So, when we come in for testing, we look for ways to get around those fraud algorithms. Can we manufacture situations where we can defeat the controls themselves?” he says.
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