I saw an article the other month about how Mastercard has started trialling a payment card that uses the same fingerprint-scanning technology as mobile phones to verify cardholders’ identities. The payments giant said the biometric card would help deliver “additional convenience and security” and “help cardholders get on with their lives knowing their payments are protected”.
I can see why Mastercard felt they had to do something. Fraud is big business and, as we discuss in the new Arvato Payments Review, it’s growing. Retailers lost 1.47% of global revenues to fraud in 2016 – up from 0.51% in 2013 – with every €100 of fraud costing you €240 (Source: 2016 LexisNexis True Cost of Fraud Study). Card fraud alone is estimated to have cost more than €15 billion in 2014 (Nilson).
That helps explain why MasterCard is trying biometrics to simplify and streamline card-based purchases. As the Arvato Payments Review shows, consumers from Helsinki to Madrid demand convenience with their security – especially online. Any friction in the buying process can see customers go elsewhere.
The most obvious measures for preventing and detecting credit card fraud are 3-D authentication and TAN (transaction authorisation number) systems, but they can also make paying a chore.
However, there are other ways to make certain that shoppers are who they claim to be that are not nearly as intrusive.
Everything from the way I swipe, zoom, type and use a mouse can confirm that it really is me behind the transaction. Matching this to previous shopping or account behaviour (passive biometrics) provides valuable insights, differentiating human traffic from non-human traffic, and separating the good guys from the bad. A decision engine can use a combination of all these to decide in real time whether to allow a purchase to proceed, with any “yellow flags” – suspicious transactions – referred to a manual order review. This provides the best possible mix of machine learning and data analytics with human insight and intuition, allowing you to understand fraud better and detect new fraud patterns.
Credit card fraud aside, you also have to take into account the risks associated with payment methods such as open invoice, which can dramatically increase conversions but also lead to a sharp rise in payment defaults.
In my home market of Germany, it is essential that retailers or their payment partners can accurately assess the credit risk of the purchaser while ensuring compliance with strict data protection laws. This requires compliant systems and processes to validate their identity and address, to assess their creditworthiness and limit exposure to bad debt. More accurate credit scoring and address management uses the latest financial data on people and businesses, combined with powerful predictive tools, to filter out risky customers. The same data can also provide valuable insights that allow retailers to build better customer relationships.
With every market having its own favourite ways to pay, and its own typical forms of risk and fraud, it’s essential to know what you are getting into. That’s why we brought together data and insights on 14 thriving e-commerce markets, looking at the big picture when it comes to Risk and Fraud, Payment Methods and Consumer Behaviour. You can download it here.