Mobile service provider Infobip and neXva, a fully managed and hosted white label app store solution provider has announced a global partnership.
Infobip is now providing in addition to their direct carrier billing platform, a fully managed and hosted solution that includes an application store and content provided by neXva. This business model is providing new revenue streams to operators, new markets for content providers and easy “billing on phone” solution for end users.
“The mobile payment market is rapidly evolving from premium SMS to direct carrier billing. Thanks to our cGate platform, Mobile Operators will now be able to easily add this new payment technology in their portfolio. We now provide a full end-to-end billing platform, from “how to bill” to “what to bill”. Through our partnership with neXva, operators now can extend their offering beyond a robust mobile payment platform to include a branded mobile app store with top quality content. The result is a benefit to the carrier’s customers and a way for the carrier to increase their revenue” said Massimo Cristini, Infobip Managing Director North America.
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