Machine learning fraud detection systems could save card issuers and banks bn annually
Published on Thursday 21st July 2016Sector: ICT/DigitalCategory: Portfolio Progress In a study published in conjuction with portfolio company featurespace, Oakhall, the London based analysis firm, estimates that global financial services firms could save at least $12 billion annually by employing adaptive, machine learning fraud management systems. Adaptive behavioural analytics software could increase the identification of actual fraudulent transactions by 25%, and reduce the number of ‘genuine transactions declined’ by more than 70% (which would also reduce the costs associated with managing blocked customers). This change in could save the industry over $12 billion from its $31 billion total annual cost of card fraud. Martina King, Featurespace CEO, commented: “Having genuine transactions declined is extremely frustrating for consumers and damages their relationship with the card issuer or bank. They also result…
Link to Full Article: Machine learning fraud detection systems could save card issuers and banks bn annually
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