Austrian mobile payment service provider DIMOCO is expanding its mobile payment network to cover another six European countries including Russia, Finland, Norway, Netherlands, Sweden and Spain. DIMOCO was founded in 2000 and specializes in billing micro amounts via mobile network operators; its mobile transaction hub operates by bundling mobile operator connections and providing them to business customers to bill their virtual and digital content. The company’s expansion, which is to take immediate effect, will expand DIMOCO’s network to reach 19 countries and approximately 583 million people. CEO Gerald Tauchner points out that the expansion will connect the company with another 313.2 million mobile subscribers and claims, “we’re well on the way to achieving global coverage.”
DIMOCO states that m-payments are particularly compatible with new industries such as browser games, social networks, online media, app developers and online dating providers, which appreciate quick and easy payment methods. The company recently announced a 261% increase in mobile payment transaction volume in the virtual goods sector.
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