Stop Billions In Fraud Losses With Machine Learning

Security and risk (S&R) professionals specializing in fraud find it increasingly difficult to develop new behavioral patterns and models to detect the telltale signs of cybercriminal activity across commerce channels — particularly mobile. This is exacerbated by many firms' inability to find or afford enough fraud data scientists. Thus, when S&R pros must adapt their own fraud models, it's a slow and inefficient process. The alternative is to wait for vendors to update the models in their commercial solutions. Both scenarios leave some businesses vulnerable to significant fraud losses for an extended period. In addition, with legacy models there is no way for S&R pros to know when the accuracy of the model has deteriorated. This is why there is so much excitement at the prospect of applying machine learning methods, algorithms, and models to fraud management. The hope is that machine learning will drastically reduce model update cycle times, which will not only improve fraud detection but give fraud analysts and investigators more time to focus their efforts on investigating suspicious transactions. In this report, we examine the promised benefits of machine learning and provide an overview of the vendors incorporating it in their technology.