
Deal builds on relationship between PayEase and Cybersource that began in 2009
Chinese payment-service provider PayEase will partner with Cybersource to provide fraud management services to protect online domestic payments in China. The Boston Consulting Group has recently stated that online retail sales in China are expected to reach US$360 billion by 2015.
The new deal builds on the relationship between PayEase and Cybersource that began in 2009 when PayEase began using Cybersource’s Decision Manager to handle cross-border transactions. The Decision Manager service detects fraudulent activity through utilising a number of tests.
“PayEase will continue to benefit from this powerful feature when it starts to use Decision Manager for managing domestic fraud. This will enable PayEase to provide a more convenient, safer method of payment in the domestic market,” said Poon Khye Wei, Regional Director of Greater China and Korea for Cybersource.
With the rapid growth of China’s online and e-commerce shopping and payments services, payment security and fraud management are vital to continued growth.
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