If you use a credit card, you probably know the feeling of having your card declined due to a suspected fraudulent transaction. An industry report from 2015 found that one out of every six legitimate cardholders experienced at least one declined transaction because of inaccurate fraud detection in the past year. That makes fraud detection an expensive problem for issuers: Those declined transactions lead to nearly $118 billion dollars in losses on an annual basis.
Even though numerous machine learning approaches have been developed in the past to address fraud, newly introduced data science automation platforms like Feature Labs give us a reason to revisit the problem. And now, any organization can see the power of automation for themselves using our just announced developer library, Featuretools.