Machine Learning Fraud Detection Systems Could Save Card Issuers and Banks 12bn 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. For the full study see http://www.featurespace.co.uk/cost-of-card-fraud. 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 via a wide range of customers, including the leading US payments processor, TSYS, as well as Vocalink/Zapp, William Hill and Betfair.