Finney, the first smartphone that is blockchain-enabled in the world, is going to soon be available for purchase in a new market, in Bangladesh. The news was made by The Daily Star, with a highlight that the Telecommunication Regulatory Commission in the country approved the smartphone in August for import. It is expected that Finney becomes available in October.

The Bangladesh launch of Finney appears as there is a growing smartphone ownership trend in the entire country. A report released in 2018 by GSMA Intelligence showed that by the year 2025, around 75% of citizens will own a smartphone. This is a significant market of 138 million people. 41% of these should have mobile internet access.

GSMA shows that the urbanized population, which is increasing, is buying smartphones as some new cheap devices are available. This is where a problem appears for Finney since its price tag is comparable to the higher-end phones. Last year, the debut of the phone was at $999. Samsung Galaxy S10 is now available in Bangladesh at $894 and the Samsung flagship phone is now blockchain-enabled.

Finney was developed by Sirin Labs, a Switzerland-based company. This happened after a huge ICO that happened in 2017 and that raised $157.8 million. The smartphone includes built-in cold storage, a completely decentralized application ecosystem and secured communications.

Moshe Hogeg, Sirin Labs co-CEO and co-founder, declared:

“Before the Finney, you needed a ledger, you needed a computer, you needed wallet software and then you needed to go on an exchange, and then you could convert. The Finney does all of this in one phone.”

Bangladesh makes sense as a new market for Finney due to the growing cryptocurrency support in the company. In August 2018, the government used money coming from its IT project fund to send selected graduates to India and Japan for blockchain training. 100 new graduates will be sent aboard in order to improve field expertise. This includes artificial intelligence, distributed ledger technology, cybersecurity and machine learning.

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