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For Security & Risk Professionals

Stop Billions In Fraud Losses With Machine Learning

Machine Learning Is The Only Way To Keep Up With Resourceful Fraudsters Committing Cross-Channel Fraud

April 6, 2015

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Why Read This Report

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.

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Table of Contents

  • Legacy Fraud Detection Methods Will Cost Firms Billions In Losses
  • Machine Learning Overcomes The Limitations Of Legacy Methods
  • Case Studies In Machine Learning And Fraud
  • Navigate The Vendor Landscape
  • RECOMMENDATIONS

  • Track Machine Learning Efficiency In Vendor Proof-Of-Concept Demos
  • WHAT IT MEANS

  • Machine Learning Will Extend To All Types Of Transaction Fraud
  • Supplemental Material
  • Related Research Documents

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