IBM acquires IRIS Analytics for fraud detection analytics

Only 16% 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 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. IRIS applies machine learning models to help analysts detect fraud and act quickly to help reduce fraudulent events. IRIS serves to bridge the gap between expert-driven rules and traditional predictive modeling by applying artificial intelligence and cognitive techniques to partner with human experts in suggesting best fit analytics interactively, while testing and deploying models with real production data, as it happens and without downtime.

IRIS Analytics provides technology and consultancy services to the payments industry to beat electronic payment fraud. It develops and markets the IRIS off-the-shelf fraud prevention platform which powers real-time fraud scoring across the full range of electronic payment channels, including credit, debit, prepaid, online and mobile. IRIS enables payment processors, switches, payment service providers, wallets and issuers across the world to analyse and respond to complex fraud patterns in real time, staying one step ahead of evolving, unanticipated fraud threats.