Significant European client gained
WEX Inc. a provider of corporate payment solutions has formed an agreement with Calder Conferences & World of Travel to provide the WEX Travel solution. Calder is recognised as one of the United Kingdom’s leading agencies for accommodation, conference, event management and business travel. The agreement emphasises WEX’s commitment to service customers across Europe with its virtual payment technology. The solution allows Calder to reduce costs, increase efficiencies, and improve security with its single use virtual card technology.
Myles Stephenson, CEO of WEX subsidiary CorporatePay said, “We are delighted to announce another significant client win in Europe. This relationship will ensure Calder has the necessary tools to help meet the business objective of efficient payments to drive customer satisfaction.”
James Turner, Commercial Director Calder said, “The WEX virtual solution offers us a flexible, secure and efficient payment system for our providers. Working with WEX allows us to further enhance our core business activities, while we continue to develop the services and tools for major partners.”
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
