Collaborating Authors

Operationalizing Analytics for Intelligent Fraud Detection and Case Management


Fraud is widespread and continues to grow, especially online. As fraudsters innovate and scale up, fraud prevention and investigation become more challenging. To discuss today's challenges and how advanced analytics helps prevent fraud, IIA spoke with Michael Ames, Senior Director of Data Science and Emerging Technologies at SAS.

How Machine Learning Combats Payment Fraud


Payment fraud evolves in ways that are truly frightening -- and with haste. Fraudsters are continually scrambling to keep up with, and have one foot in front of, technology, with recent stumbling blocks in the form of EMV, in the United States, likely to give rise to greater card-not-present fraud. But the would-be payments criminals have potent weaponry at hand, including ever faster, ever more powerful and ever cheaper computing power, and they have been targeting, according to data science firm Feedzai, the weaker links that exist in the financial services chain. In a recent whitepaper titled "A Primer to Machine Learning for Fraud Management," the firm noted that, even as financial services evolve to embrace a digital world -- with, say, virtual goods in hand and even virtual cash -- the prospects for successful payments malfeasance grow in lockstep. In fact, said Feedzai, as many as 65 percent of firms with annual revenues of at least 1 billion were victims of payments fraud as recently as 2014.

Countering Remittance Frauds with an Enterprise-wide Fraud Management Approach


Cybercrime continues to be an unending botheration for banks. While the focus of attempts and attacks until recently, tended to be on the banks' customers (via card and account detail compromises), of late fraudsters have become more sophisticated and have raised the stakes. They have shifted their focus and are now directly targeting banks. They have begun deploying increasingly sophisticated methods of circumventing individual controls in the banks' local environments and have probed deeper into systems to execute well-planned and finely orchestrated attacks. One area where fraudsters have increased malicious attacks is Correspondent Banking, especially via SWIFT.



These days it's often proclaimed to be the next big thing in fraud management. The only bit of that you'll find me disagreeing with is the word'next'. Machine learning's been at the heart of the CyberSource approach to fraud management nearly since the beginning of its development. Machine learning underpins fraud scores produced by Decision Manager, our fraud management platform. In this three-part blog series, I'll first provide a quick primer on what machine learning is.