IBM has made its first acquisition of the new year, buying German vendor IRIS Analytics for its anti-payment fraud technology. Despite the heightened awareness of payment fraud in recent years, it remains a serious problem, as IBM says in its annoucement"
Only 16 percent of banks polled in a recent global IBM Institute of Business Value study could detect fraud as it is attempted. Once new schemes were identified and confirmed, it is estimated that countermeasures typically require over four weeks to deploy.
Existing fraud detection techniques are constrained by the dependency on specialized “black box” models that are hard to understand, explain, and adapt. As the payments industry continues to evolve with faster, alternative and mobile payments innovations and the expanding use of chip & PIN, fraudsters have become more technologically sophisticated and organized, adapting their fraud techniques more quickly.
IRIS provides a real-time fraud analytics engine that leverages machine learning to generate rapid anti-fraud models while also supporting the creation and modification of ad-hoc models, proven successful on various sized payment platforms. Acting as a “virtual analyst” to help identify new fraud patterns, IRIS applies machine learning models to help analysts detect fraud and act quickly to help reduce fraudulent events.
Analysis: The IRIS Acquisition Speaks to Broader Analytics Trends
It would be a mistake to view the IRIS deal as just something to bolster IBM's product set for the financial services vertical.
"This is another sign of the growing importance of streaming scenarios and, thus, the need for real-time analytical capabilities," says Constellation Research VP and principal analyst Doug Henschen. "This deal complements technologies that IBM acquired years ago, such as SPSS predictive analytics and ILog rules technology, providing a way to create dynamic systems that can rapidly respond to changing conditions. In this instance the use case is fighting fraud, but the cognitive capability to spot and respond to changing conditions in real time will increasingly be in demand in many application settings."
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