
Transformational deal
North American mobile banking and payment provider Clairmail is entering into a definitive acquisition agreement with UK mobile money network technology and services company Monitise (view press release). The deal will cost Monitise $173 million and will bring the combined entities’ consumer base to 13 million people across four continents. Combined, the two companies process billions of transactions a year and around $10 billion of payments and transfers on an average weekly basis. On completion of the deal, the integrated parties will provide mobile money services to a third of the top 50 North American financial institutions, including 8 of the top 13.
The deal enhances Monitise’s reach in North America through Clairmail’s direct sales channel and strong relationships with financial organisations. Together with strategic partnerships with Visa and FIS, Monitise is now positioned with three commanding routes to market.
“The deal is transformational of our customers, our team, our shareholders and our company,” says Monitise CEO, Alastair Lukes. “It is great news for all those wanting to offer bank- grade mobile money services to billions of consumers, not only in the US but worldwide.”
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