Machine Learning Innovations Drive 30% CNP Performance Lift

Blog Post created by Advocate on Apr 2, 2018

Recent FICO research reiterates that card-not-present (CNP) fraud is growing dramatically as a percentage of total fraud losses. In EMEA specifically, CNP fraud now accounts for 70% of total card fraud losses, up from 50% in 2008. At the account level, this figure is close to 90%. Similar CNP metrics exist around the globe.


While issuers often avoid high dollar liability for CNP fraud, they have a strong interest in CNP fraud prevention as an improved consumer experience benefits all parties. As a result, payment processors, card issuers, and merchants are all striving to improve the separation of fraudulent and legitimate CNP transactions.

In support of these efforts, FICO has developed new machine learning techniques focused specifically on CNP transactions. These advances have demonstrated an ability to reduce total CNP fraud losses by upwards of 30% without increasing false positive rates. The CNP machine learning innovations will be included in the 2018 consortium models (both Credit and Debit) and will be available in the standard consortium model release cycle. There are no incremental licensing or upgrade fees associated with this release.