Telecommunications company, Orange Madagascar, is partnering with money transfer initiative company, MFS Africa, to launch an online money transfer service, reports Finextra. The partnership will enable customers to receive international remittances directly to their Orange Money accounts. Set up in 2010, Orange Money allows domestic transfers and currently serves around one million customers. The international remittance service will extend the benefits of Orange Money to the Malgache diaspora community in France and elsewhere. To use the service, international customers register on the website, proceed to pay with most major bank cards, and remit funds to Orange Money accounts in Madagascar. MFS Africa will provide and operate the web portal which is connected to the Orange Money platform in Madagascar.
“Our intention with Orange Money Transfer International is to lower the cost of sending money back to Madagascar while making it more convenient for both senders and receivers. We believe this ultimately benefits Madagascar Diaspora members and their families back home” said Jean-Luc Bohe, CEO of Orange Madagascar.
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