Machine learning fraud detection systems could save card issuers and banks billion annually

Adaptive behavioural analytics software reduces ‘genuine transactions declined’ by over 70% and incidence of undetected fraud by 25% 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 according to a study published in conjunction with Featurespace. By employing adaptive behavioural analytics software to both identify actual fraudulent transactions, and reduce the number of ‘genuine transactions declined’ – as well as reducing the costs associated with managing blocked customers – the industry could reduce the $31 billion total annual cost of card fraud by over $12 billion annually.Featurespace is a world leader in adaptive behavioural analytics software. Its services and products are employed in over 180 countries to a wide range of customers,…


Link to Full Article: Machine learning fraud detection systems could save card issuers and banks billion annually