Monday, March 29, 2021

The IMF Is Thinking About Your Digital Footprint As A Credit Report

 First up the press release, December 17, 2020:

What is Really New in Fintech

.... Recent IMF and ECB staff research distinguishes two areas of financial innovation. One is information: new tools to collect and analyse data on customers, for example for determining creditworthiness. Another is communication: new approaches to customer relationships and the distribution of financial products. We argue that each dimension contains some transformative components.

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 nonfinancial 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....


Sounds pretty benign, advancing financial inclusion and all

And from the working paper (beginning on page 10 of the PDF:

Financial Intermediation and Technology:What’s Old, What’s New?

....New developments

Technological progress perpetuates the trend toward a greater use of hard information in finance, with both its benefits and drawbacks. As the volume of codified information increases, its use will expand from the current realm of standardized products such as mortgages into more complex business segments such as commercial lending and financial advisory services. While this will further increase efficiency and reduce costs, it can also amplify existing incentive problems or create new ones.

The use of non-financial data will have large effects on the provision of financial services. Traditionally, banks rely on the analysis of customer financial information from payment flows and accounting records. The rise of the internet permits the use of new types of non-financial customer data, such as browsing histories and onlines hopping behavior of individuals, or customer ratings for online vendors. 

The literature suggests that such non-financial data are valuable for financial decision making. Berg et al. (2019) show that easy-to-collect information such as the so-called “digital footprint” (email provider, mobile carrier, operating system, etc.) performs as well as traditional credit scores in assessing borrower risk. Moreover, there are complementarities between financial and non-financial data: combining credit scores and digital footprint further improves loan default predictions. Accordingly, the incorporation of non-financial data can lead to significant efficiency gains in financial intermediation.5

Large technology firms collect vast amounts of non-financial data through their consumer-facing platforms in the areas of e-commerce, social networking, and online search. The sheer amount of data enables the use of “big data” analysis tools such as artificial intelligence and machine learning. The literature confirms their usefulness in finance. For example, Dobbie et al. (2018) show that the use of machine learning can lead to significantly improved default risk predictions. 

Accordingly, Bigtech firms have the informational capacity to compete, and possibly even outperform banks in financial service provision. This is corroborated by Frost et al. (2019), who show that the internal ratings of MercadoLibre, an online marketplace in Latin America, predict default risk better than credit scores. There is evidence that Bigtech finance is most effective when traditional financial intermediation is undersupplied. Hau et al. (2019) document that Ant Financial, an arm of the Alibaba online marketplace, extends more credit lines in rural areas of China with a limited presence of banks.

Finally, the proliferation of the internet increases the overall availability of public information; large amounts of data can be acquired at low cost via web-scraping.....

....MUCH MORE (32 page PDF)

 Huh. I'm not seeing any of that inclusion lingo.