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Dynamic Detection for the Global Fraud Fight

Those of you who follow this blog know that I regularly discuss analytic innovations that boost fraud detection. One such innovation is adaptive analytics, so-called because these models continually "adapt" traditional neural network fraud models in response to real-time fraud tactics that were not present at the time of model training. This helps financial institutions combat newer fraud schemes that arise between fraud model developments.

FICO research regularly demonstrates that adaptive models provide performance improvements and extend the useful lifetime of static neural network fraud models. Results from our latest research study show that these results hold true worldwide.

More specifically, the study showed that banking institutions worldwide can significantly improve fraud detection rates and reduce transaction false positives. We analyzed consortium data supplied by FICO® Falcon® International Credit clients, comparing the addition of adaptive models to using only neural network models.

Figure 1 below shows how adding adaptive models improved fraud detection rates at Account False Positive Ratios (AFPR) of 10:1 and 20:1. Results are broken down by clients from various geographies.


Similarly, Figure 2 shows how adding adaptive models reduced Transaction False Positive Ratios (TFPR) at fixed fraud Transaction Detection Rates (TDR) of 15% and 30%.


Better detection at lower false positive ratios translates, of course, into lower fraud losses and less negative impact to banking customers. International Credit models for FICO® Falcon® Fraud Manager 6 will incorporate a new adaptive analytics capability later this year, allowing our clients to keep up with the latest fraud schemes in their respective regions.

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