Visa, MasterCard and American Express want to make online shopping safer for consumers.
Visa, MasterCard and American Express are calling on other industry figures to join them in their support of a new framework for the global token standard, which they believe will make life simpler and safer for customers shopping online.
The framework would see issuers, merchants and digital wallet providers able to ask for a token, so that when a customer goes to make an online or mobile transaction, the token would be used in place of a traditional card account number to process, authorise, clear and settle payment. The standard used to generate the tokens would be based on existing industry standards and would be available across all networks and wider market participants.
Ed McLaughlin, chief emerging payments officer, MasterCard, says: “This continued transition from plastic cards to digital is all about providing consumers with the ability to easily and safely make a purchase. They would no longer need to store their actual card account number when shopping online or with a smart device; the token would serve as that stand-in.”
Whitepapers
Related reading
Central banks best suited to issue digital currencies
By Aaran Fronda A recent report by the Official Monetary and Financial Institutions Forum (OMFIF) said that central banks rather than private ... read more
Instant payments: innovations inbound for corporates
In 2020, instant payments look set to continue their current trajectory to become the biggest trend in payments. While these schemes already offer numerous benefits to corporates, leveraging innovations such as APIs and request to pay will go some way to unlocking their full potential, argues Michael Knetsch
Obstacles exist for banks to meet ECB’s instant payments goal
The cost of joining instant payment platforms will be one of many hurdles banks and payment services providers must overcome to meet ... read more
Banks must be aware of “biases” in data used to train ML models
Financial institutions need to be conscious of biases in the historical data that is being used to train machine learning (ML) models, ... read more
