Partner with Ural Bank.
Seamless has signed an agreement with one of Russia’s largest regional banks, Ural Bank to launch the mobile payment service SEQR in the Russian market.
The Ural Bank based in Yekaterinburg is one of Russia’s five largest regional banks and operates mainly in the Sverdlovsk region. Part of bank’s strategies for the coming two years is to double its active customers and to position the bank as a reliable financial institution with modern high quality products and services.
“The Russian market is not only one of the largest but also one of the most attractive, as credit card penetration is so low there. There is therefore considerable interest in supplementing costly cash management systems with payments by mobile phone,” says Peter Fredell, CEO of Seamless and continues: “SEQR has previously concluded agreements with financial institutions in Sweden, Finland, Norway, Kuwait, Belgium, Rumania and Malaysia. Since being launched in Sweden in 2012, it has been possible to pay via SEQR in thousands of stores, with an additional 3,500 stores contracted for roll-out. In October, SEQR was launched in Romania in partnership with Garanti Bank and a launch in collaboration with bpost bank in Belgium is planned in spring 2014.”
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
