Financial and Monetary Systems

As Big Tech gets into banking, these are the risks to watch out for

Big Tech are taking the finance industry by surprise as they have the advantage of big data. Image: REUTERS/Stefan Wermuth

Jon Frost

Senior Economist, BIS

Leonardo Gambacorta

Head of Innovation and the Digital Economy, Bank for International Settlements

Huang Yi

Executive Vice-President, China Construction Bank

Hyun Song Shin

Economic Adviser and Head of Research, Bank for International Settlements

Pablo Zbinden

Head of the Credit Risk, Mercado Crédito

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Financial and Monetary Systems

The bars indicate annual global lending flows by Big Tech and other FinTech firms over 2013-2017. Figure includes estimates. 1 Total FinTech credit is defined as the sum of the flow of Big Tech and other FinTech credit. This is then divided by the stock of total credit to the private non-financial sector. 2 Calculated for countries for which data were available for 2013–2017.
Image: Cambridge Centre for Alternative Finance and research partners, Big Tech companies’ financial statements, authors’ calculations.
 The bars visualise the estimated change in Big Tech and other FinTech credit volumes from a change in the respective variables, based on the estimated coefficients displayed in the fifth column of Table 3. 1 Change in Big Tech credit and other FinTech credit per capita given a one-standard deviation change in the selected variables. 2 Nominal GDP in USD over total population. Given the non-linearity of the relationship, the change is calculated at the average GDP per capita level. 3 Regulatory stringency is constructed as an index based on the World Bank’s Bank Regulation and Supervision Survey. The index takes a value between 0 (least stringent) and 1 (most stringent) based on 18 questions about bank capital requirements, the legal powers of supervisory agencies, etc. 4 One-standard deviation increase in the banking Sector Lerner index (an indicator of bank mark-ups and hence market power).
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1 The loss rate is the volume of loans more than 30 days past due relative to the origination volume. In its use to date, the internal rating of Mercado Libre is better able to predict such losses. It segments the originations into five different risk groups as compared to the three clusters identified by the credit bureau. The size of the dots is proportional to the share of the firms in the rating distribution. 2 True positive rates versus false positive rates for borrowers at different thresholds for a logistic model with only the credit bureau score (I), a logistic model with the bureau score and borrowers’ characteristics (II), and a machine learning model with the Mercado Libre credit score (III). A random model is included for comparison purposes. The ROC curve shows that the machine learning model has superior predictive power to both the credit bureau score only and the credit bureau score with borrower characteristics.
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