SAS Fraud Management employs a "Self-Organising Neural Network Arboretum" (SONNA) modelling capability to build its unique hybrid approach to consortium and custom models.

SAS Fraud Management offers significant improvement in fraud detection performance compared to the performance of regular, non-linear modelling techniques like neural networks. A "score-on-demand" process helps card issuers obtain updated scores based upon the passage of time.

The SAS solution provides the creation of risk-based reason factor groups, giving an organisation's fraud operations and analytics groups more insight and context to the risks and reasons behind the model score - optimising responses and automating actions to a high-scoring transaction.

"With new types of fraud threats on the rise, such as man-in-the-middle and man-in-the-browser, financial institutions are going to need more sophisticated analytics to monitor all transactions and to constantly be looking for abnormalities in behaviour," said George Tubin, Senior Research Director for TowerGroup, a Corporate Executive Board Company. "Vendors that employ enhanced analytical approaches are critical in preventing these types of attacks."

"Banks will be able to better protect themselves and their customers against fraudsters using these advanced techniques," said Revathi Subramanian, primary patent inventor and Research and Development Director in the SAS Fraud Modelling Department. "This patent is a demonstration of SAS' continued commitment to innovation and reinvestment that makes it one of the foremost business analytics companies in the world."

SAS has developed consortium models for Asia Pacific, Mexico, the United Kingdom and the United States, but the models can also be used in other non-listed regions to drive immediate return on investment.

"SAS feels that customised predictive models are the best way to maximise ROI from a fraud solution," said Ellen Joyner, Global Marketing Manager, Financial Crimmes Prevention at SAS. "We understand that all clients may not have the data history of known fraudsters and their behaviour to customise predictive models or the requirement for a customised approach. SAS has developed regional models based upon consortium data that helps SAS to readily deploy 'out of the box' analytics across multiple product and channel portfolios."