INTRODUCTION
MACHINE LEARNING (ML) as a buzzword is appearing more and more throughout the business and social world. However, despite growing interest, Machine Learning isn’t new at all. In fact, the model itsel f has been around since the 1970s and ‘80s. In the financial sector, banks have been using ML to mitigate fraud and detect irregular buyer behaviors and patterns for the last decade or more. Fraud is a growing concern and is costing the financial sector millions of dollars in losses each year. A 2015 research note from Barclays stated: “The U.S. is responsible for 47 percent of the world’s card fraud despite accounting for
relying on sophisticated AI measures to evolve, adapt and learn in line with the behavior patterns of fraudsters in order to track, detect and prevent fraud far more quickly than traditional methods. Obviously, it is in the interest of the card issuer or bank to implement strategies that reduce the risk of fraud. Unfortunately, this often requires a compromise between expense and convenience to both the merchant and the customer. Merchants are at far more risk than the end credit card user as they are ultimately responsible for incurring the cost of a fraudulent purchase and often suffer from loss of revenue and consumer trust.
only 24 percent of total worldwide card volume.”
As technology evolves, so do the cunning methods for perpetrating a fraudulent crime. Financial firms are now
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