Card Fraud Global Cost Increases 29% to $40 Billion
London-based analysis firm Oakhall has estimated that the cost of card fraud for the card industry increased by 29% to $40.1 billion in 2015 compared to the $31 billion in 2014.
This was primarily driven by the continuous rise in card use, a larger proportion of online shopping, and the growing sophistication of fraud criminals. The study was published in conjunction with Featurespace; the global leader in machine learning adaptive behavioural analytics fraud prevention software.
Costs associated with incidents of card fraud in 2015 increased 34% to $21.8 billion according to The Nilson Report, and according to Oakhall, all fraud-related costs associated with genuine transactions declined increased 24% to $18.3 billion.
Jonathan Crossfield, partner at Okahall, commented on these statistics: “With card fraud costing the industry 7 cents for every $100 spent in 2015, we expect banks to continue to invest in more advanced fraud detection and prevention systems.”
He added: “80% of card fraud in the UK happens in ‘card not present’ online or phone transactions where existing systems often trigger higher numbers of genuine transactions declined, resulting in customer dissatisfaction and lost income for the banks.”
CEO of Featurespace, Martina King, also commented: “Card fraud is becoming more prolific and far more costly – an almost $10bn increase in the cost of card fraud in just 12 months. In the UK alone, we will see card fraud continue to escalate, both in volume and sophistication.
“Our adaptive machine learning fraud prevention software has been proven to protect banks revenues and substantially cut operational costs from false fraud alerts. It also helps the banks maintain positive relationships with their customers.”
With the use of industry data, Oakhall estimated that global financial services firms could save at least $15.8 billion annually (compared to $12.2 billion in 2014) by employing adaptive, machine learning fraud prevention software. The estimated savings comprising of $5.5 billion reduced fraud (compared to $4.1 billion in 2014), and $10.3 billion reduced fraud management costs and lost revenue ($8.1 billion in 2014).
Using historical customer data from a multinational retail bank, results showed that the Featurespace machine learning software reduced undetected fraud incidents by 25% compared to the bank’s existing fraud management system. The results also demonstrated that Featurespace’s fraud prevention system reduced the incidents of genuine transactions declined by over 70%.
According to the Oakhall study, through machine learning, adaptive behavioural analytics software, card issuers will be able to reduce genuine transactions declined, improve operational efficiencies, and lower the incidence of undetected fraud.