1、 BIS Working Papers No 887 Fintech and big tech credit: a new database by Giulio Cornelli, Jon Frost, Leonardo Gambacorta, Raghavendra Rau, Robert Wardrop and Tania Ziegler Monetary and Economic Department September 2020 JEL classification: E51, G23, O31. Keywords: fintech, big tech, credit, data, t
2、echnology, digital innovation. BIS Working Papers are written by members of the Monetary and Economic Department of the Bank for International Settlements, and from time to time by other economists, and are published by the Bank. The papers are on subjects of topical interest and are technical in ch
3、aracter. The views expressed in them are those of their authors and not necessarily the views of the BIS. This publication is available on the BIS website (www.bis.org). Bank for International Settlements 2020. All rights reserved. Brief excerpts may be reproduced or translated provided the source i
4、s stated. ISSN 1020-0959 (print) ISSN 1682-7678 (online) Fintech and big tech credit: a new database 1 Fintech and big tech credit: a new database1 Giulio Cornelli, Jon Frost,* Leonardo Gambacorta, Raghavendra Rau,* Robert Wardrop* and Tania Ziegler* Bank for International Settlements,* Cambridge Ce
5、ntre for Alternative Finance, CEPR Abstract Fintech and big tech platforms have expanded their lending around the world. We estimate that the flow of these new forms of credit reached USD 223 billion and USD 572 billion in 2019, respectively. China, the United States and the United Kingdom are the l
6、argest markets for fintech credit. Big tech credit is growing fast in China, Japan, Korea, Southeast Asia and some countries in Africa and Latin America. Cross-country panel regressions show that such lending is more developed in countries with higher GDP per capita (at a declining rate), where bank
7、ing sector mark-ups are higher and where banking regulation is less stringent. Fintech credit is larger where there are fewer bank branches per capita. We also find that fintech and big tech credit are more developed where the ease of doing business is greater, and investor protection disclosure and
8、 the efficiency of the judicial system are more advanced, the bank credit- to-deposit ratio is lower and where bond and equity markets are more developed. Overall, alternative credit seems to complement other forms of credit, rather than substitute for them. Keywords: fintech, big tech, credit, data
9、, technology, digital innovation. JEL classification: E51, G23, O31. 1 The views are those of the authors and not necessarily of the Bank for International Settlements. We gratefully acknowledge comments and input from Raphael Auer, Tobias Berg, Marcel Bluhm, Stijn Claessens, Sebastian Doerr, Boris
10、Hofmann, Martin Hood, Pawee Jenweeranon, Ross Leckow, Loriana Pelizzon, Jermy Prenio, Antoinette Schoar, Jose Maria Serena, Ren Stulz, Cheng-Yun Tsang and an anonymous referee, and participants at the Deutsche Bundesbank conference “Banking and Payments in the Digital World”, a Zhejiang University I
11、nternational Business School webinar, a Vaduz Roundtable and a BIS research meeting. We thank Stephen Ambore, Masaki Bessho, Cyprian Brytan, Iuliia Burkova, Teresa Caminero, Greg Chen, Anrich Daseman, Graeme Denny, Darren Flood, Sergio Gorjn Rivas, Aleksi Grym, Cheryl Ho, Tobias Irrcher, Arif Ismail
12、, Chandan Kumar, Lyu Yuan, Nur Fazila Mat Salleh, Nicolas Mme, Manoranjan Mishra, Aiaze Mitha, Irina Mnohoghitnei, Mu Changchun, Michelle ODonnell Keating, Vichett Oung, Jisoo Park, Naphongthawat Phothikit, Melchor Plabasan, Bintang Prabowo, Ricky Satria, Martina Sherman, Paul Shi, Joshua Slive, Ylv
13、a Svik, Edward Tan, Rupert Taylor, Triyono, Vicente de Villa, Chris Welch, Maarten Willemen and Melanie Wulff for help with data for individual jurisdictions. We thank Tyler Aveni, Matas Fernandez, Gil Guan, Daisy Mwanzia, Devyani Parameshwar and Huiya Yao for assistance with company-level data. We
14、thank Haiwei Cao and Yuuki Ikeda for research assistance. Corresponding author: Jon Frost, jon.frostbis.org; Centralbahnplatz 2, 4002 Basel, Switzerland. Fintech and big tech credit: a new database 2 1. Introduction Credit markets around the world are undergoing a transformation. While banks, credit
15、 unions and other traditional lenders remain the chief source of finance for companies and households in most economies (with capital markets playing an important role in some cases), new intermediaries have recently emerged. In particular, digital lending models such as peer-to-peer (P2P)/marketpla
16、ce lending and invoice trading have grown in many economies in the past decade. These types of credit, facilitated by online platforms rather than traditional banks or lending companies, are referred to as “debt- based alternative finance” (Wardrop et al., 2015) or “fintech credit” (Claessens et al.
17、, 2018). Moreover, in the past few years, many large companies whose primarily business is technology (“big techs”) have entered credit markets, lending either directly or in partnership with financial institutions (BIS, 2019; Stulz, 2019). While these digital markets and business models often use n
18、ew sources of data for credit scoring, an irony is that data on their overall size are notably scarce. There are well-developed systems for official reporting of bank lending volumes (flow) and credit outstanding (stock). Recently, there have been efforts to improve the data on non-bank credit to th
19、e private sector (Dembiermont et al., 2013; FSB, 2020) and on fintech (Serena, 2019; IFC, 2020). Central banks and public sector authorities use such data to monitor economic and financial conditions, to guide monetary policy decisions and to set macroprudential policies, such as the countercyclical
20、 capital buffer.2 Yet for fintech and big tech credit, authorities often rely on non-official sources. Some individual fintech credit platforms voluntarily publish detailed data on their loan portfolios, but these are generally not comparable across platforms and reporting is not standardised across
21、 jurisdictions. The most comparable data on fintech credit volumes come from the Cambridge Centre for Alternative Finance (CCAF), e.g. Rau (2020) and Ziegler et al. (2020). These data, based on surveys of platforms around the world, provide annual flows of new lending. Claessens et al. (2018) use CC
22、AF, Brismo and WDZJ data. Data on big tech credit volumes are patchy. Frost et al. (2019) have assembled estimates of big tech credit for 2017, and sought to explain volumes in a cross-country setting. We are not aware of any other comparable cross-country data sources on big tech credit. The lack o
23、f data on these new forms of credit is at odds with the macroeconomic relevance of credit markets. By allocating resources to allow for productive investment and consumption smoothing, credit contributes to economic growth and welfare (Levine, 2005). Yet when credit in an economy expands too rapidly
24、 (a credit boom), this can be a harbinger of a financial crisis and severe recession (see Drehmann et al., 2010; Schularick and Taylor, 2012; Kindleberger and Aliber, 2015). In order to detect credit booms in real time, authorities need adequate information on lending. As fintech and big tech credit
25、 become more economically relevant, it will become ever more important to have sound data on flow and stock of loans and other credit characteristics (interest rates, defaults, margins etc.). In this paper, we assemble and update available data on fintech and big tech credit volumes for a large numb
26、er of countries around the world. The database is then used to answer the questions: how large are fintech and big tech credit markets, in absolute 2 The countercyclical capital buffer sets bank capital requirements that are higher in periods of high credit growth, when financial vulnerabilities may
27、 build up, and can be released during a downturn. The buffer is set by authorities based on the credit-to-GDP gap (a measure of credit market conditions) and supervisory judgment. See Drehmann and Tsatsaronis (2014). Fintech and big tech credit: a new database 3 terms and relative to overall credit
28、markets? What economic and institutional factors are driving their growth and adoption? How large and important could they become in the future? There are key differences between the two types of credit. Fintech credit models were originally built around decentralised platforms where individual lend
29、ers choose borrowers or projects to lend to in a market framework. Platforms help to solve problems of asymmetric information both through their screening practices, and by providing investors with information on the risk of a loan and other borrower characteristics. Over time, some platforms have m
30、oved to fund loans from institutional investors rather than only individuals, and many use increasingly sophisticated credit models (see e.g. Jagtiani and Lemieux, 2019). Yet the core business of fintech credit platforms remains financial services. Big tech firms, by contrast, have a range of busine
31、ss lines, of which lending represents only one (often small) part, while their core business activity is typically of a non-financial nature. These firms have an existing user base, which facilitates the process of onboarding borrowers. They can use large-scale micro-level data on users, often obtai
32、ned from non-financial activities, to mitigate asymmetric information problems. While these large volumes of information allow big tech firms to effectively measure loan quality and potentially reduce loan defaults, it is also plausible that they could raise problems of price discrimination (Morse a
33、nd Pence, 2020; Philippon, 2019), and concomitant issues for competition and data privacy (Carstens, 2018; BIS, 2019; Petralia et al., 2019; Boissay et al., 2020).3 Policymakers will need to weigh the efficient loan supply potential in their economies against issues of discrimination, competition an
34、d privacy when deciding which types of credit to encourage. For both fintech and big tech credit, understanding the size and growth of these markets is of fundamental importance for policymakers who monitor markets and set monetary and macroprudential policies based on credit aggregates. Such data a
35、re also essential for research on credit and digital innovation. A key contribution of this paper is thus to assemble estimates on the size of these markets and make these available for policymakers and researchers as a public good. Our main findings are as follows. First, we estimate that, in 2019,
36、 fintech and big tech credit (together “total alternative credit”) reached USD 795 billion globally. Big tech (USD 572 billion) has shown particularly rapid growth in Asia (China, Japan, Korea and Southeast Asia), and some countries in Africa and Latin America. Global fintech credit volumes (USD 223
37、 billion) have actually declined in 201819 due to market and regulatory developments in China. Outside China, fintech credit is still growing. We also show that returns to investors in fintech credit have declined over time, and that big tech firms show much higher profit margins in their overall bu
38、siness. This, together with their large volumes of platform, may be one factor in the overall growth of big techs. To understand the drivers of this growth, we run cross-country panel regressions of fintech and big tech credit for 79 countries over 201318. We distinguish between supply and demand dr
39、ivers, and hypothesise that fintech and big tech credit should be higher where it is more attractive for new intermediaries to offer credit, and where there is an un(der)met demand for credit. We find that such alternative forms of credit are more developed in countries with higher GDP per capita (a
40、t a declining rate), where banking sector mark-ups are higher and where banking regulation is less stringent. 3 As a further illustration of the issues for competition, Kamepalli et al. (2020) show how high-priced acquisitions by incumbents (e.g. digital platforms) may actually deter the funding of
41、new entrants. Fintech and big tech credit: a new database 4 Regulation and banking sector mark-ups are relatively more important for fintech credit. Fintech credit is also more developed where there are fewer bank branches per capita. We find that fintech and big tech credit are more developed where
42、 ease of doing business is greater, investor protection disclosure and the efficiency of the judicial system are more advanced, the bank credit-to-deposit ratio is lower, and where bond and equity markets are more developed. Based on our results, we look ahead at how large these markets may become i
43、n the future. Overall, alternative credit seems to complement other forms of credit, rather than substitute for them. These alternative forms of credit contract seem to help complete the market and are mostly demand- driven. We discuss how rapid credit growth may raise risks to financial stability.
44、We argue that the Covid-19 pandemic may accelerate the growth of big tech credit in particular. The rest of the paper is organised as follows. Section 2 discusses the construction of our database and explores trends in fintech and big tech credit markets. Section 3 illustrates an empirical analysis
45、of the drivers of fintech and big tech credit volumes over time. Section 4 concludes with policy implications and avenues for future research. Some methodological notes on data construction are set out in Annex A and the results of robustness checks in Annex B. 2. Database construction and credit ma
46、rket trends This section discusses the data collection for our study, and analyses trends in fintech and big tech credit markets. In particular, it describes the sources used and necessary choices on data aggregation and estimation. It discusses the growth in global volumes and in credit across diff
47、erent regions and countries, including the economic importance of such markets relative to the stock of total credit. It then reviews the pricing of fintech and big tech credit, and the performance to date in terms of credit defaults and profit margins. Data collection The data on fintech credit com
48、e from the Global Alternative Finance Database (2013 18) held at the Cambridge Centre for Alternative Finance (CCAF). Data are collected from an annual industry survey and web-scraping by CCAF and academic partners (Wardrop et al., 2015; Ziegler et al., 2018; Zhang et al., 2018; Ziegler et al., 2020
49、). Firms are asked in an online questionnaire to report annual alternative finance volumes, with 11 time- series required questions that serve to pinpoint exact transaction values, the number of stakeholders etc. All loan-based business models are counted as fintech credit. This includes peer-to-peer (P2P) or marketplace lending to consumers, businesses or for property; balance sheet lending
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