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Cryptocurrencies more famous in corrupt countries: IMF

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Cryptocurrencies are more popular in countries deemed corrupt or have strict capital controls, the International Monetary Fund (IMF) said in a recent research report, reinforcing the case for increased regulation of the industry.

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The IMF explained that the report shows that countries expect intermediaries such as cryptocurrency exchanges to adhere to “know your customer” procedures, a standard of authentication designed to prevent fraud, money laundering, and terrorist financing. Countries such as the United States have already instituted such controls.

Moreover, Countries around the world are currently struggling to find the best way to regulate the huge cryptocurrency market, which is now worth more than $2 trillion and is expected to reach $22 trillion by 2026.

However, due to the uneven level of regulation in various countries, this makes it quite easy to abuse cryptocurrencies in certain countries and regions of the world, leading to various frauds and Ponzi schemes.

The IMF said the findings suggest that crypto-assets “ may be used to transfer corrupt proceeds or circumvent capital controls ” and that residents of countries with developed traditional financial industries may feel less need for cryptocurrencies.

The IMF also said it extracted baseline data on cryptocurrency use from information collected by the German firm Statista. The survey covered 55 countries, each with 2,000 to 12,000 respondents. Respondents were asked if they own or use digital assets in 2020.

The report also found reasons why cryptocurrencies are more popular in certain countries. First, high inflation could mean that popular cryptocurrencies like Bitcoin are more stable than local currencies.

Second, since poorer countries also tend to have stricter capital controls to prevent foreign inflows and domestic outflows, cryptocurrencies may also be a way to circumvent taxes and restrictions.

However, the IMF warned that while its findings are noteworthy, they should be interpreted with caution given the small sample size and data quality cannot be guaranteed to be accurate.

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