
Extending services to the cloud
The operating subsidiary of Trunkbow, a provider of mobile payment and mobile value added solutions in China, is entering an agreement with Shanghai Telecommunication Engineering (STE) for the construction, management and operation of a cloud based data centre in Shanghai, China (view press release). This data centre will provide hosted mobile payment solutions for both m-commerce and POS transactions. By extending its services to the cloud, Trunkbow will enable SMBs to utilise their technology under SaaS and PaaS models.
Under the agreement, Trunkbow will invest approximately RMB 180 million (USD 28 million) in the 8,262 square metre data centre and STE will provide the design and construction services. Trunkbow will receive transaction fees and a fixed percentage of the hosting fees paid by merchants for use of the cloud services. STE will also provide a minimum revenue guarantee of RMB 304.3 million (USD 47.9 million) over the initial ten-year contract term. STE will operate the facility and provide maintenance services through a partnership with China Telecom.
Construction of the new data centre will begin towards the end of the year and is expected to be completed within twelve months.
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