The independent foreign exchange company, RationalFX has recently launched Xendapay.com, a cheap method of transferring money around the world. The website enables customers to send up to 10% more money for the same price when compared with the high street money transfer outlets.
Customers can send up to £2500 in each transfer, which can be made at any time and in one of the following 8 languages: English, French, Spanish, German, Italian, Polish and Chinese. The website is more competitive than the high street as there are no agents or branches charging for the service, money is transferred using least-cost routing and exchange rates are obtained wholesale on the currency markets.
Online standard transfers are not charged and online express transfers cost £9.99. Those who wish to send more than £2500 can upgrade to Xendpay+ at no extra cost.
The website is also launching a partner service with banks overseas to enable unbanked customers to collect cash via bank outlets.
UK consumers transfer up to £4 billion each year with the majority going to the Asian sub-continent, the Caribbean, Africa, China and Eastern Europe.
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
