KXEN, a global provider of predictive and descriptive analytics software, today announced that its KXEN Analytic Framework has been implemented by Identity Risk Management provider ID Analytics.
The KXEN Analytic Framework was selected by ID Analytics for its flexibility, ease of use and speed in helping to make sense of large amounts of data from varying sources.
ID Analytics, which has developed the nation's first and only real-time network for identity fraud prevention, will use KXEN predictive analytics capabilities for data validation to help establish baseline performance metrics and as a quick way to assess new variables and assist in variable screening. KXEN allows users to access many data source formats and to generate descriptive and predictive models extremely fast.
KXEN is a particularly valuable tool for a problem such as identity fraud. For example, one of the primary methods for preventing identity theft and fraud is to identify inconsistencies in customer applications and authentication, which requires reviewing data from any number of sources. Such a process is nearly impossible to accomplish manually and with traditional methods of predictive analytics because of the lack of ability to review large numbers of variables in an efficient manner.
"The KXEN Analytic Framework is one of the many tools ID Analytics uses in analyzing data," said Xuhui Shao, vice president of analytics, ID Analytics. "The flexibility of KXEN has allowed for a quick and simple integration, providing us with an effective tool for establishing baseline performance and variable assessment of data."
"ID Analytics, with a proven track record of innovation in providing real-time fraud prevention, is an ideal environment to demonstrate the power of KXEN" said Joerg Rathenberg, vice-president, KXEN. "ID Analytics has identified a perfect use case to highlight KXEN's mission of making data mining a part of everyday business decisions."
Technology industry analyst IDC predicts that the predictive analytics market will grow to more than $3 billion by 2008, citing companies' growing need to extend analytics capabilities to non-technical staff, such as line of business professionals.