The use of machine learning (ML) analytics is one of the biggest buzzwords in fraud prevention. And why not? The concept is sound—fraud is moving too fast for the legacy approaches that rely on rules and annual model refreshes to be effective. Financial institutions (FIs) and merchants need advanced analytics technology that can evolve rapidly and keep pace with the progression of fraud attacks so they can prevent losses while maintaining a positive customer experience.
The good news is that there is a lot of substance behind this particular buzzword. ML enables models to learn on an iterative basis and, therefore, is proving quite effective at enhancing fraud mitigation efforts. The success is such that those that do not invest in this technology risk being left behind, as their competition that have embraced it are able to provide superior customer experiences. Confusion abounds, however, as is the case when a complex concept achieves buzz status. “Machine learning” is now the marketing slogan du jour, and as a result, it means many different things to many different people.
This Impact Note cuts through the vendor hype and marketing fluff to help readers truly understand the use of ML in the fraud mitigation arena. It puts forth a definition of the technology, maps various vendor approaches into a set of typologies, and describes concrete use cases and proof points that illustrate its value.
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