IMF wants your online search history to determine your credit score ...social credit system here we come?
From the IMF site: Dec. 17, 2020
"New types of information
The most transformative information innovation is the increase in use of new types of data coming from the digital footprint of customers? various online activities?mainly for credit-worthiness analysis.
Credit scoring using so-called hard information (income, employment time, assets and debts) is nothing new. Typically, the more data is available, the more accurate is the assessment. But this method has two problems. First, hard information tends to be ?procyclical?: it boosts credit expansion in good times but exacerbates contraction during downturns.
The second and most complex problem is that certain kinds of people, like new entrepreneurs, innovators and many informal workers might not have enough hard data available. Even a well-paid expatriate moving to the United States can be caught in the conundrum of not getting a credit card for lack of credit record, and not having a credit record for lack of credit cards.
Fintech resolves the dilemma by tapping various non-financial data: the type of browser and hardware used to access the internet, the history of online searches and purchases. Recent research documents that, once powered by artificial intelligence and machine learning, these alternative data sources are often superior than traditional credit assessment methods, and can advance financial inclusion, by, for example, enabling more credit to informal workers and households and firms in rural areas."
https://blogs.imf.org/2020/12/17/what-is-really-new-in-fintech/
From the IMF site: Dec. 17, 2020
"New types of information
The most transformative information innovation is the increase in use of new types of data coming from the digital footprint of customers? various online activities?mainly for credit-worthiness analysis.
Credit scoring using so-called hard information (income, employment time, assets and debts) is nothing new. Typically, the more data is available, the more accurate is the assessment. But this method has two problems. First, hard information tends to be ?procyclical?: it boosts credit expansion in good times but exacerbates contraction during downturns.
The second and most complex problem is that certain kinds of people, like new entrepreneurs, innovators and many informal workers might not have enough hard data available. Even a well-paid expatriate moving to the United States can be caught in the conundrum of not getting a credit card for lack of credit record, and not having a credit record for lack of credit cards.
Fintech resolves the dilemma by tapping various non-financial data: the type of browser and hardware used to access the internet, the history of online searches and purchases. Recent research documents that, once powered by artificial intelligence and machine learning, these alternative data sources are often superior than traditional credit assessment methods, and can advance financial inclusion, by, for example, enabling more credit to informal workers and households and firms in rural areas."
https://blogs.imf.org/2020/12/17/what-is-really-new-in-fintech/