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, technology,
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 character. 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 ().
© Bank for International Settlements 2020. All rights reserved. Brief excerpts may be
reproduced or translated provided the source is stated.
ISSN 1020-0959 (print)
ISSN 1682-7678 (online)
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 Centre 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 largest 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 banking 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 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, 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 Hofmann, Martin Hood, Pawee Jenweeranon, Ross Leckow, Loriana Pelizzon, Jermy
Prenio, Antoinette Schoar, Jose Maria Serena, René 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 International 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 Gorjón Rivas, Aleksi Grym, Cheryl Ho, Tobias Irrcher, Arif
Ismail, Chandan Kumar, Lyu Yuan, Nur Fazila Mat Salleh, Nicolas Même, Manoranjan Mishra, Aiaze Mitha,
Irina Mnohoghitnei, Mu Changchun, Michelle O'Donnell Keating, Vichett Oung, Jisoo Park, Naphongthawat
Phothikit, Melchor Plabasan, Bintang Prabowo, Ricky Satria, Martina Sherman, Paul Shi, Joshua Slive, Ylva
Søvik, 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, Matías Fernandez, Gil Guan, Daisy
Mwanzia, Devyani Parameshwar and Huiya Yao for assistance with company-level data. We thank Haiwei Cao
and Yuuki Ikeda for research assistance.
Corresponding author: Jon Frost, @; Centralbahnplatz 2, 4002 Basel, Switzerland.
1. Introduction
Credit markets around the world are undergoing a transformation. While banks, credit 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)/marketplace 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., 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 new 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 the 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 capital 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 jurisdictions. The most comparable data on fintech credit
volumes come from the Cambridge Centre for Alternative Finance (CCAF), . 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 CCAF, 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 of 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 (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 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 number 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 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).
terms and relative to overall credit 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 lenders 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 moved to fund loans from institutional investors rather than only
individuals, and many use increasingly sophisticated credit models (see . Jagtiani and
Lemieux, 2019). Yet the core business of fintech credit platforms remains financial services.
Big tech firms, by contrast, have a range of business 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 obtained
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 and 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 and 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 are 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, 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
billion) have actually declined in 2018–19 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 business. 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 2013–18. We distinguish between supply and
demand drivers, 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 (at 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 (. digital platforms) may actually deter the funding of new entrants.
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 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 in
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. 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 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 market 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
different 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 come 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). 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 to
consumers, businesses or for property; invoice trading, debt-based securities (debentures and
bonds) and mini-bonds. Equity-based, donation-based and reward-based crowdfunding are not
included in fintech credit. This excludes profit-sharing crowdfunding, community shares,
pension-led funding and real estate crowdfunding, which are counted in the broader category of
alternative finance.
CCAF survey responses for 2019 are currently being collected and are hence not yet
available. As such, fintech credit volumes for 2019 have been estimated based on 2018 volumes
and more recent data from Brismo, the People’s Bank of China, Reserve Bank
of India, Bank Indonesia and the Korea P2P Lending Association. Annex A gives further
details.
The data on big tech credit have been collected from contacts at central banks and big tech
firms, and from a variety of publicly available sources. The former were collected under
agreements to keep company-specific figures confidential. Because most big tech firms do not
report their lending flows by country and year, and many do not even file annual reports, we
have had to rely on partial information and assumptions in some cases. These include the
following:
- Where only end-year stocks of outstanding credit are available, we have
estimated the credit flow as the difference between these stocks, plus those loans
assumed to have matured over the year based on estimates of the average loan
maturity (see Annex A for the methodology used). Available numbers for firms
with both stock and flow data suggest that this is a very reasonable assumption.
Additionally, we have cross-checked these aggregate estimates with central banks
directly.
- Where a firm only reports its total lending over several countries, we have
distributed this lending volume proportional to its revenue in those countries or,
where this is not available, proportional to the GDP of these countries. Where data
were available, we checked that the growth in revenues was aligned with the growth in
the number of unique users. In several cases, we have cross- checked these estimates
with the central bank or a regulator in the country in question.
- Where 2019 lending volumes were not yet available, we have assumed that growth
is proportional to the number of overall users. Where this is not available, we
have extrapolated 2019 volumes with the growth rate over 2017/2018. From
those platforms with data for 2017, 2018 and 2019, we can confirm that this is a
reasonable assumption.
Big tech firms in our sample have credit activities in 31 countries as of 2019 and include
Airtel, Amazon, Alibaba/Ant Group, Apple, Au Jibun Bank, Baidu/Du Xiaoman, BKash
Facebook, Fuse, Go-Jek, Google, Grab, , Jumia, Kakao Bank, K-bank, LINE, Mercado
Libre, Microsoft, MTN, MTS bank, Mynt, Ola Cabs, Orange, Ovo, Ozon, Rakuten, Samsung,
STC, Tencent/WeBank, Telenor, Tigo, Tokopedia, Toss, Uber,
Vodacom/Vodafone (M-Pesa) and Yandex. Not all of these companies have lending
activities.
Because of the patchy nature of disclosure by big tech firms, and the assumptions needed
to estimate fintech and big tech credit volumes, there will necessarily be some measurement
error. In particular, it is possible that some big tech firms or some of their activities are not
captured in our sample. Moreover, in those cases where lending flows have been estimated, it
may be that actual lending flows differ from estimates based on end-year stocks, and that the
distribution of firms’ activities across countries or their 2019 growth differs from our
estimation. Overall, these factors may mean that our database tends to underestimate
actual alternative credit volumes. Based on the uncertainty around the 2019 estimates
and the unavailability of some of our independent variables, we conduct our regressions
only for the period 2013–18. We have higher confidence around the lending flow estimates than
around the stocks. Flows are made available in the database; stock estimates are available upon
request.
Growth in global volumes
Available data and estimates show that fintech credit volumes reached USD 297 billion in
2018, while big tech credit volumes surged to USD 397 This represents a dramatic
increase since 2013, when volumes were only USD billion and billion, respectively.
Combined lending by fintech credit platforms and big tech companies thus reached USD 694
billion in 2018. While still small overall, fintech and big tech lending flows are much larger
relative to the stock of lending in certain economies, notably China and Kenya (see next sub-
section). We estimate (subject to somewhat greater uncertainty) that the stock of fintech
and big tech credit reached USD 303 billion at the end of 2018, or about % of the stock of
overall credit to the private sector in markets around the world.
Global big tech credit is booming, overtaking fintech credit Graph 1
Big tech credit is overtaking fintech credit1 These alternative forms of lending are becoming a
significant portion of total credit in a few economies
USD bn USD m, logarithmic scale Per cent
Figures include estimates. CN = China, US = United States, JP = Japan, KR = Korea, GB = United Kingdom, ID = Indonesia, NL = Netherlands, RU =
Russia, KE = Kenya, DE = Germany.
1 2019 fintech lending volume figures are estimated on AU, CN, EU, GB, NZ and US. 2 Data for 2019. 3 Domestic credit provided by the financial
sector. Data for 2018. 4 Total alternative credit is defined as the sum of fintech and big tech credit. Data for 2019.
Sources: IMF World Economic Outlook; World Bank; ; Cambridge Centre for Alternative Finance and research partners; ;
companies’ reports; authors’ calculations.
Based on the estimates for 2019, big tech credit volumes may have been as large as USD
572 billion in 2019. While definitive fintech credit volumes are not yet available, estimates
based on higher-frequency sources yield a global volume of USD 223 billion. This means that
big tech credit was at least twice as large as fintech credit in 2019 (Graph 1, left-hand panel).
There is substantial variation across countries, with the sum of fintech and big tech credit flows
(“total alternative credit”) equivalent to % of the stock of
4 Throughout the paper, fintech and big tech credit volumes refer to the flow of new lending over a calendar
year. This differs from the standard methods for reporting bank credit, which are end-year stocks of loans
outstanding. A quirk of the fintech and big tech lending market is that firms are more likely to report the
accumulative loan flow over a year or since the inception of their business, rather than a current outstanding
loan book. This also because contract maturities tend to be quite short. For more details, see Annex A.
total credit in Kenya, % in China and % in In other major markets like the
United States, Japan, Korea and the UK, fintech and big tech lending flows are less than 1% of
the stock of total credit.
Growth in volumes by geography
The largest market for both fintech credit and big tech credit is China. Big tech
companies like Alibaba’s Ant Group, Tencent’s WeBank, Baidu’s Du Xiaoman and e-
commerce platform lent USD 363 billion in 2018 and 516 billion in 2019,
according to data provided by the People’s Bank of China. This covered a wide range of
borrower types, from small businesses on Alibaba’s Taobao platform (Ant Group) to
smartphone-based consumer loans (WeBank) to rural student loans (Du Xiaoman). Ant Group
and WeBank, in particular, were able to make use of the extensive payments data from their
mobile payment services to price credit (see Frost et al., 2019). Fintech credit (P2P) platforms,
which had been numerous and fast-growing in the period through 2017, actually contracted
their lending in 2018 and 2019, as a series of defaults and platform failures took their toll
on the sector. From a peak of 3,600 fintech credit platforms in November 2015, only 343
were still in operation in December 2019, with steady exits but no new platform entries since
September 2018 (Graph 2, left-hand panel). New lending was only at 17% of its peak (July
2017) level, while the stock of loans was at 46% of its (May 2018) peak (Graph 2, right-hand
panel). Of the lending that has still happened, loan tenors have steadily risen, reaching
months, at an average interest rate of %. It is expected that P2P lending in China will
continue to decline over 2020. Because of the (previously) large size of the Chinese fintech
credit market, this has a large impact on global fintech credit volumes. Based on our
method for converting flows to stocks, the stock of fintech and big tech credit may have been
as large as USD billion at end-2019. Notably, due to the short tenors of many big tech
loans relative to fintech (P2P) loans, the stock of fintech credit may still have been larger than
the stock of big tech credit.
The United States is the second largest market for fintech credit, but big tech credit volumes
are relatively small – certainly compared with the economy’s deep credit markets. Fintech
credit reached USD billion in the United States in 2019 (up from USD billion in
2018). This was made up primarily of P2P/marketplace consumer lending, with investment
coming predominantly from institutional investors rather than individual lenders. It came in
large part from platforms like Lending Club, SoFi, Prosper and OnDeck. These platforms often
partner with financial institutions, originating loans that are sold on to banks and other
institutional While the United States is home to many of the largest big tech
companies in the world, only Amazon engaged in any significant lending in 2018, to the tune of
roughly USD 1 billion, according to public reporting.
5 In Indonesia, big tech credit activities are performed in partnership with financial institutions. Big tech firms are
prohibited by law from direct lending.
6 Another large group of lenders, not captured in our fintech credit data, are fintech mortgage lenders such as
Quicken Loans, Amerisave, Cashcall, Guaranteed Rate, Homeward Residential and Move Mortgage, which
often originate loans for the government-sponsored enterprises (GSEs) like Fannie Mae and Freddie Mac. See
Buchak et al. (2018) and Fuster et al. (2018).
Fintech credit platforms continue to exit in China, as stock and flow of loans fall Graph 2
Number of fintech credit platforms in China Stock and flow of loans is falling as average tenors rise
Number of platforms Number of entries/exits RMB bn Maturity in months
Data based on all platforms covered by for China.
Sources: ; authors’ calculations.
As of 2019, Apple launched its Apple Card in cooperation with Goldman Sachs, which
had outstanding balances of USD 7 billion by Plans by Google to offer a checking
account product, in conjunction with Citi, Stanford Federal Credit Union and several other
banks, and a cooperation between Amazon and Goldman Sachs on small business lending may
be significant in the future, but were not yet in operation. Uber Money began to offer payment
and wallet products to its drivers, but executives noted that lending was not yet on the roadmap
(Shevlin, 2019). The stock of fintech and big tech credit together reached an estimated USD
billion at end-2019.
The second-largest market for big tech credit is Japan (USD billion of lending in
2019). In Japan, Rakuten has offered a suite of financial products since 2013, including payments,
transaction lending, credit card issuing and acquiring, mortgages and insurance.
Meanwhile, social media company LINE offers consumer lending (through a joint venture
with Mizuho Bank and a credit card company), telecommunication firm NTT DoCoMo
provides customer credit-scoring services (upon contractual agreement with banks and
customers) and Amazon lends through its seller lending programme. The second largest
telecommunications provider, KDDI, has a joint venture with MUFG Bank called Au Jibun
Fintech credit (estimated at USD billion in 2019) in Japan is primarily through
P2P/marketplace business and property
In Korea, big tech credit (lending flow of USD billion in 2019) is provided by two
major virtual banks – KakaoBank and KBank, which launched in 2017 by messaging platform
Kakao and telecommunication company KT, respectively. These firms offer loans with a
relatively short maturity (roughly one year on average) to users of their respective networks.
Information on their lending is provided publicly by the Financial
7 The stock of fintech and big tech credit together reached an estimated USD billion at end-2019.
8 This bank is counted in our sample starting on 1 April 2019, when it became a consolidated subsidiary of KDDI.
The announcement emphasized that the bank would be able to benefit from the big data and user network of
KDDI – consistent with the characteristics of a number of other big tech lenders.
9 The stock of fintech and big tech credit is estimated to have reached USD billion at the end of 2019.
Supervisory Service and Bank of Korea. Fintech credit volumes reached about USD billion
in 2019, and the market is dominated by P2P/marketplace property
The UK, meanwhile, had estimated fintech credit volumes of USD billion in 2019
(up from USD billion in 2018), made up of a vibrant mix of P2P/marketplace business,
consumer and property lending, and smaller volumes of balance sheet lending and invoice
trading. After rapid growth in 2013–16, fintech lending volumes have been relatively steady in
the United Kingdom in the past three years, perhaps reflecting greater maturity and
saturation in the relevant market segments. For instance, Ziegler et al. (2020) estimate that
fintech credit platforms accounted for up to % of equivalent bank credit to small and
medium enterprises with annual turnover below GBP 2 million in 2018. This may have been
encouraged by public policy; for instance, the government-owned British Business Bank
invested over GBP 165 million over 2014–18 for lending through Funding Circle, a UK credit
platform, and announced a commitment for a further GBP 150 million to support small
business lending (British Business Bank, 2018). Big tech credit volumes are estimated to be
much smaller, at an estimated USD 100 million in 2017 and 2018, primarily through Amazon’s
Seller Lending programme.
Looking beyond the largest fintech and big tech credit markets, higher-frequency data
from Brismo and WDZJ show that fintech credit volumes have continued to grow rapidly in
the European Union, Australia and New Zealand, even as they have plateaued in the United
States and United Kingdom and declined in China (Graph 3, left-hand panel). In many
emerging market and developing countries (not shown), fintech lenders are becoming
economically significant lenders for specific segments, such as small and medium-sized
enterprises (SMEs) (Cornelli et al., 2019; World Bank, 2020). Some fulfil so-called agency
banking functions, by which they function as agents to expand the reach of banks, especially
in Latin America and parts of Asia and Africa.
Fintech credit is growing in Europe, big tech credit is booming in Asia Graph 3
Fintech lending volumes are diverging1 Big tech credit is booming in Asia, the United States and
Africa2
Index, Q1 2017 = 100 USD mn, logarithmic scale
CN = China, JP = Japan, KR = Korea, US = United States, KE = Kenya, ID = Indonesia.
1 Data are based on five platforms for Australia and New Zealand, all platforms covered by for China, 49 platforms for Europe, 34 for the
United Kingdom and five for the United States. Volumes are reported in local currency. 2 Figures include estimates.
Source: ; ; companies’ reports; authors’ calculations.
10 The stock of fintech and big tech credit is estimated at USD billion as of end-2019.
Big tech credit is achieving economically significant scale in China, Japan, Korea, parts
of Southeast Asia, East Africa and (to a lesser extent) some countries in Latin America
(Graph 3, right-hand panel). This is driven by the lending activities of e- commerce
platforms like Mercado Libre, ride-hailing companies like Grab and Go-Jek, and
telecommunication and mobile money providers like M-Pesa. In many cases, these lenders
initially target a specific group of users (. sellers on the e-commerce platform, or drivers) but
then expand such credit offerings to more users over time.
Interest rates, defaults and margins
Information on interest rates, defaults and profit margins is not available for all countries in the
sample, but available data can give some useful insights.
The interest rates charged on fintech credit appear to be roughly in line with
comparable bank loans. For borrowers, as of the latest readings by CCAF, typical interest rates
charged on the major US fintech credit platforms range between 9 and 28%. In the UK, interest
rates are between and 24%. In China, interest rates have been more volatile in past years
given the changes in the market and regulation (Gambacorta et al., 2019). Data on the interest
rates charged by big tech companies are not available. Looking across the largest fintech
credit markets, it is apparent that the returns paid to investors in fintech credit platforms have
been relatively high in the past five years, but are trending downward globally (Graph 4, left-
hand panel). For big tech companies, the return on loans could also include the benefit
obtained from supporting companies’ core business lines (e-commerce, social media,
advertising etc.), user loyalty to the platform’s overall services, and user data.
Big tech firms are highly profitable, while fintech platforms have often struggled Graph 4
Returns on fintech credit platforms have trended down Big tech firms are more profitable than fintech platforms
Per cent Per cent
1 Average interest rate. 2 Simple average of Black Knight Financial Services, Elevate, Enova International, Fellow Finance, Funding Circle,
LendingClub, Lendingtree, Nelnet, OnDeck and Synchrony. 3 Simple average of Alibaba, Amazon, Apple, Baidu/Du Xiaoman, Facebook, Google,
, Kakao, LINE, Microsoft, MTS bank, Orange, Rakuten, Samsung, Tencent, Uber, Vodacom, Vodafone and Yandex.
Source: ; Refinitiv Eikon; ; authors’ calculations.
Defaults at fintech credit platforms have picked up in the past few years. More granular
default data show that certain loans segments like US consumer lending have seen a worsening
of credit quality in the past three years; high-frequency data on the impact of the Covid-19
pandemic on credit quality are not yet available. At the same time, available empirical
evidence suggests that some fintech and big tech lenders, through the use of alternative data
and machine learning, have been able to achieve lower default rates than with traditional data
and models, and even to achieve superior performance after a downturn in the credit cycle
(Gambacorta et al., 2019). It is an open question how credit models will perform in the current
downturn.
For fintech and big tech platforms to continue to grow, intermediation needs to be
profitable for the providers (a separate consideration than the return provided to
investors). In this light, the profit margins of big tech firms (that benefit, however, from a more
diversified bundle of activities) are relatively high. Margins are particularly high when
compared with those of fintech credit platforms, which have often struggled to achieve
profitability (Graph 4, right-hand panel) and have relied on new investor funding for expansion.
For big tech firms, this often relates to the high profit margins in core businesses lines. In
some cases, there are questions about whether big tech platforms wish to engage directly in
lending at all, since it is less profitable than these other activities (FSB, 2019). This, as well
as regulations, may be a factor behind the use of partnership models, where the big tech
distributes financial products but a financial institution retains such lending on its balance
sheet.
3. Drivers of credit volumes across economies: a panel
analysis
In this section, we seek to explain fintech and big tech volumes in different economies over
time. This is a novelty with respect to earlier studies (Claessens et al., 2018; Frost et al., 2019)
that analyse such volumes in the cross section. Leveraging on the new database and
following Rau (2020), we extend the analysis using a panel approach. We look at the drivers of
fintech and big tech separately, and then take a deeper look at a range of specific country
characteristics that are most salient in the cross-section dimension, for their sum (total
alternative credit).
We hypothesise that fintech and big tech credit per capita can be broadly related to
demand side and supply side drivers. On the demand side, we expect that more developed
economies (with higher GDP per capita) will have a higher demand for credit from firms and
households, but that this relationship may show a decreasing trend for very high levels of
development (see Claessens et al., 2018; Frost et al., 2019; Bazarbash and Beaton, 2020).
Similarly, we expect that fintech and big tech credit will be higher when incumbent banking
services are more expensive (higher banking sector mark-ups), for instance because of less
competition, and where there is a larger un(der)met demand for financial services, as proxied by
fewer bank branches per capita. On the supply side, we expect that more stringent banking
regulation (a proxy for the overall stance of financial regulation) will create barriers to the
entry for fintech and big tech firms. A number of additional institutional characteristics, such
as the ease of doing business, investor protection and disclosure, the judicial system and
characteristics of the incumbent banking system will be discussed later. We will try also to
answer the open
,
−1
,
−1
question of whether fintech and big tech credit complement or substitute for bank credit and other
forms of
Our baseline regression takes the form:
ln( ) = + 1 ,−1 + 2 2 + ,−1 + ,−1 +
,−1 + ,−1 + + (1)
where is the volume of fintech or big tech credit per capita in economy i at time t, or
total alternative credit. Thus, we consider three credit aggregates as left-hand side variables,
each with the same
The right-hand side includes a number of regressors that are lagged by one year to mitigate
endogeneity issues. ,−1 is the GDP per capita in economy i at year t-1, and the variable
2 is its quadratic term, to address the non-linear relationship between credit development
and income levels. ,−1 is the Lerner index13 of banking sector mark-ups in economy i,
reflecting market power by incumbent banks; a higher value may reflect a less competitive
banking sector. ,−1 is an index of regulatory stringency for the banking sector of economy
i, as constructed by Barba Navaretti et al. (2017) from World Bank , is the density
of the bank branch network in country i compared with the adult population (which may capture
both the reach of the banking sector and its relative cost base). , is a vector of control
variables that includes: growth in GDP and total credit; a real short-term interest rate; a
dummy for whether a country had suffered a financial crisis since 2006, as defined by Laeven
and Valencia (2018); mobile phone subscriptions (given the mobile-based nature of many
platforms); and a dummy for advanced economies. Bank branches and mobile subscriptions are
measured relative to the adult is a vector of geographical area fixed effects and
, is an error
Table 1 reports descriptive statistics for our sample of 79 countries over the period 2013–
18. Given the lower coverage of many variables for 2019, we have excluded this year from
regressions. Data come from a variety of sources, including the IMF’s World Economic
Outlook and the World Bank’s Global Financial Development Database (GFDD) and
Findex.
11 For Germany, De Roure et al. (2016) find that P2P lending substitutes the banking sector for high-risk consumer
loans. De Roure et al. (2018) present a theoretical model and further evidence in favour of such “bottom
fishing”. For the United States, Tang (2019) finds that P2P lending is a substitute for bank lending in terms of
serving infra-marginal bank borrowers, but that it complements bank lending with respect to small loans.
12 Frost et al. (2019) refer to the sum of fintech and big tech credit as “total fintech credit”. Here, to prevent confusion,
we refer to the sum of fintech and big tech credit as “total alternative credit”. We also regress bank credit per capita
using the same specification to check for significant differences.
13 The Lerner Index of banking sector mark-ups has been updated over the period 2015–17 using
information on the alternative cyclical measure devolped by Igan et al. (2020). See Annex A. A higher value
indicates higher margins and profitability among traditional banks, and thus less competition.
14 The regulatory stringency variable is constructed as an index (normalised between 0 and 1) 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 22 questions (2011 survey) or 23 questions (2019 survey) about bank capital
requirements, disclosure, the legal powers of supervisory agencies etc.
15 When observation for bank branches, mobiles and credit growth were not yet available, we extrapolated the figures
using the cross-country average growth rate or the cross-country average change.
16 The inclusion of some (barely) time-invariant country-specific regressors (see next section) prevents us from
using a complete set of country dummies.
Descriptive statistics Table 1
Variable Observations Mean Standard
deviation
Min Max
GDP per capita (in thousands of USD) 453
Lerner index1 453
Bank branches per 100,000 adults 453
Normalised index of bank regulatory
stringency2
453
Score-Starting a business (overall) 425
Score-Time (days) 425
Score-Paid-in Minimum capital (% of
income per capita)
425
Score-Cost (% of income per capita) 425
Extent of disclosure index (0-10) 425
Trial and judgment (days) 425
Enforcement of judgment (days) 425
Enforcement fees (% of claim) 425
Bank credit to bank deposits (%) 212
Bank regulatory capital to risk-weighted
assets (%)
197
Provisions to non-performing loans (%) 187
Loans from non-resident banks to
GDP (%)
194
Proportion of firms with a transactions
account (%)
276 18 100
Corporate bond average maturity
(years)
135
Corporate bond issuance volume to
GDP (%)
137
Total factoring volume to GDP (%) 145
Global leasing volume to GDP (%) 78
Stock market total value traded to
GDP (%)
167
Stock market turnover ratio (%) 161
Ln(Total alternative credit per capita (in
USD)3)
453
Ln(Big tech credit per capita (in USD)) 453
Ln(Fintech credit per capita (in USD)) 453
Ln = natural logarithm. The dependent variables have been winsorised at the 1% and 99% level.
1 The Lerner index of banking sector mark-ups in economy i reflects market power by incumbent banks. World Bank data. For 2015–17, data are estimated
based on Igan et el (2020). 2 The index is normalised between 0 (no regulation) and 1 (max regulation). The index is calculated from a survey conducted by
the World Bank in given years, and therefore data are not available over the whole sample period, but proceed in steps. See
3 Defined as the sum of big tech and fintech credit.
Sources: IMF, World Economic Outlook, World Bank; Cambridge Centre for Alternative Finance and research partners; company statements; authors’
estimates.
Empirical results on fintech and big tech credit
Table 2 reports our panel regression results. It starts with estimations for total alternative credit
(big tech plus fintech credit; column 1), and then big tech credit (column 2) and fintech credit
(column 3). A formal test for differences between the coefficients for big tech credit and
fintech credit is also reported (column 4).
As in Claessens et al. (2018) and Frost et al. (2019), we find that total alternative credit
activity, as well as its two component parts (big tech and fintech credit), is positively
associated with GDP per capita, but at a declining rate. Since GDP per capita is likely to be a
proxy for many aspects of a country’s stage of development, this result confirms a positive
relationship between a country’s overall economic and institutional development and these new
credit activities. The negative coefficient estimate on GDP per capita squared suggests that the
link becomes less important and even slightly negative at higher levels of development.
Interestingly, the relationship between fintech credit per capita and GDP per capita remains
positive up to a level of GDP per capita of around USD 52,000. A total of 73 out of the 79
countries in our database have a level of GDP per capita below this threshold. This means that,
for the vast majority of countries, there is still a positive relationship between fintech credit
and GDP per capita. On the other hand, big tech credit per capita peaks at a level of USD
37,600 per capita; 20 of the 79 countries are above this threshold. This may relate to the high
values of big tech in countries toward the middle of the income distribution, such as China,
Korea and several Southeast Asian economies, and the (so far) very low big tech credit volumes
in high-income jurisdictions such as Singapore, Hong Kong SAR and most of those in
continental Europe. The difference between the estimated coefficient for fintech and big tech
credit for GDP per capita is statistically significant. As we will show later, these differences
are also detected in the cross-sectional
Meanwhile, the positive correlation with the Lerner index of banking sector mark- ups
suggests that these two alternative forms of credit, other things being equal, are more
developed in those jurisdictions with a less competitive banking sector. This result could be
explained by demand side factors, . the notion that these forms of credit are more flexible
than bank credit, and that the ease and speed of receiving the loan decision is higher – an
element that could be particularly important for underbanked clients. Moreover, the repayment
obligations tend to be more flexible than with a bank (. no penalty for early repayment etc.).
It may also be that, on the supply side, high margins make entry more attractive for the fintech
and big tech firms, themselves. Bank mark-ups explain around 5% of the variability of total
alternative credit per capita (the overall R2 is %). The elasticity of big tech credit to the
Lerner index is lower than for fintech credit, and this difference is significant at the 95% level.
The density of the bank branch network is negatively correlated with the
development of fintech credit, but not of big tech credit. The difference between the two
coefficients is statistically significant. This is consistent with the view that fintech credit
serves clients in underbanked areas and that it is therefore complementary to traditional bank
credit. This is also consistent with the use of agency banking. Big tech credit, while also
relying on digital distribution channels rather than physical branches, does not appear to be
correlated with the number of bank branches relative to the adult
17 These differences are not due to a different average size of fintech and big tech loans. Both forms of credit are
typically granted to households and small enterprises and are of modest average loan size. See Gambacorta et al.
(2020) for the case of China.
population, all else equal. This result could depend upon the global nature of big techs business
models that could reduce the link with domestic bank distribution conditions.
Drivers of fintech and big tech credit volumes
All variables are expressed in current USD, except where indicated Table 2
Ln(total alternative
credit per capita)
Ln(big tech credit
per capita5)
Ln(fintech credit per
capita6)
Difference │b-a│
(a) (b) H0: │b-a│<0
GDP per capita1 ***
()
***
()
***
()
***
()
GDP per capita^2 *** *** ***
() () () ()
Lerner index2 *** ** *** **
() () () ()
Bank branches per 100,000 *** *** ***
adult population () () () ()
Normalised regulation index3 *** *** *** ***
() () () ()
Other controls4 Yes Yes Yes
Geographic area fixed effects7 Yes Yes Yes
No. of observations 453 453 453
Estimation method OLS OLS OLS
R2
Estimation period 2013–18. Robust standard errors in parentheses. ***/**/* denotes results significant at the 1/5/10% level. Ln = natural logarithm.
The dependent variables have been winsorised at the 1% and 99% level.
1 GDP per capita (in USD thousands). 2 Lerner index of banking sector mark-ups in economy i, reflecting market power by incumbent banks. 3
The index is normalised between 0 (no regulation) and 1 (max regulation). 4 Other controls include: GDP growth; a crisis dummy that takes the value of
1 if the country was hit by the GFC and 0 elsewhere; total banking credit growth to the private non–financial sector; mobile phones per 100 persons; a
dummy that takes the value of 1 for advanced economies and 0 elsewhere; and country-specific real interest rates. 5 Big tech credit is zero in 47 countries.
To allow the computation of the log of the ratio (not defined for zero), big tech credit has been rescaled summing an arbitrary constant (the minimum
value). 6 Fintech credit is defined as credit activity facilitated by electronic platforms that are not operated by commercial banks or big tech firms. 7
The sample has been divided into five geographical areas: Africa, Asia Pacific, Europe, Latin America, Middle East and North America.
Sources: CCAF; IMF, World Economic Outlook; World Bank; authors’ calculations.
The coefficient of the stringency of banking regulation is negative for both forms of
credit: more stringent banking regulation is significantly linked to less big tech and fintech
credit activity. Banking regulation explains around 10% of the variability of total alternative
credit per capita in the baseline model, and contributes to more than one fifth of the R2. This
result, similar to that found by Barba Navaretti et al. (2017), Claessens et al. (2018) and Frost et
al. (2019), could have several possible explanations. This could suggest that regulation of
alternative forms of credit in general is more liberal in jurisdictions where banking
regulation is more liberal. Conversely, it may be more difficult to launch new lending
activities in countries with relatively strict prudential and bank licensing regimes. The
elasticity of big tech credit to the regulatory index is significantly lower than for fintech
credit. The quantitative effects of regulation are also economically significant. Specifically, a
country with an index that is one standard
deviation lower in the cross section (looser regulation) has a ratio of total alternative credit
per capita that is percentage points
The additional controls are generally not significant and are not reported in the table.
Overall, our estimations are able to explain % of the variation of fintech credit but only
% of the variation of big tech credit. This may relate to the smaller number of countries and
years in which big tech credit is present, and the fact that big tech firms operate in different
countries. This could reduce the capacity of domestic controls to capture the global nature of
big tech business models.
Results are qualitatively very similar when looking at a simple cross section,
obtained by averaging all values over 2013–18 (Table B1 in Annex B). This robustness check
is particularly relevant to mitigate endogeneity concerns. Overall, the results can be broadly
confirmed. The results are also robust to the inclusion of a complete set of time dummies that,
however, tend to capture the common global trend in the evolution of these forms of credit (see
Table B2 in Annex B).
Table B3 in Annex B reports the results when the log of the stock of bank credit to the
private sector is considered as a dependent variable. The relationship between bank credit per
capita and GDP per capita remains positive up to a level of GDP per capita of around USD
48,700. A total of 71 out of the 79 countries in our database have a level of GDP per capita
below this threshold. This means that for the vast majority of countries, there is still a positive
relationship between bank credit and GDP per capita. The negative correlation with the Lerner
index of banking sector mark-ups suggests that, other things being equal, bank credit is more
developed in those jurisdictions with a more competitive banking sector. As expected, the
density of the bank branch network is positively correlated with the development of bank
credit. Finally, more stringent banking regulation is significantly associated with lower bank
credit activity.
As a final check we consider the impact of explicit fintech regulation. Following Rau
(2020), we include in model (1) a dummy variable that takes a value of 1 if an explicit
regulation of fintech credit (“crowdfunding debt models”) was in place in a given country and
year, and 0 elsewhere. During our sample period, 21 countries introduced explicit regulation
for fintech credit. The results in Table B4 in Annex B indicate a positive correlation
between explicit regulation and fintech credit. Due to potential endogeneity issues, we do not
claim a clear causal relationship from the introduction of a fintech regulation to greater
fintech credit volumes; indeed, regulatory initiatives could take place as a specific reaction
to the development of fintech credit markets. Rau (2020) assesses this in more depth and uses
a matching and instrumental variable approach to establish at least partial causality. To our end,
what is important is that the main results of the study remain unaffected when controlling for
explicit regulation of fintech credit.
18 We have also investigated the effects of regulation that is specific to fintech and big tech credit, but at the
moment, information is scarce. Surveys from Rowan et al. (2019) and Ehrentraud et al. (2020) provide relevant
insights on regulatory frameworks in 2019. However, this is later than the sample period for our regressions, which
ends in 2018 and the surveys do not report systematically when these regulations were introduced. Regulations
that were only enacted shortly before the survey would not be expected to influence fintech and big tech credit
volumes over 2013-2018. Rau (2020) estimates the year that dedicated frameworks for debt and equity
crowdfunding were enacted and does find a significantly positive link with actual volumes one year later. This
remains an important area for further investigation.
Controlling for country institutional characteristics
In this section, we include additional country characteristics in the baseline model (1). This
represents a novelty with respect to the literature so far, which focuses primarily on cross-
sectional analysis and a very limited number of explanatory variables. While we continue to
use the full range of country-year observations, we zoom in on the impact of specific
indicators that could be highly correlated with one another. To avoid multicollinearity
problems, we therefore include these relevant country-specific characteristic one at a
time. In particular, we consider:
Barriers to entry, as expressed by the ease of doing business variables (World
Bank, 2019);
Investor disclosure and efficiency of the judicial system;
More specific characteristics of the banking sector; and
Bond and equity market development.
Descriptive statistics for these variables are included in Table
First, we evaluate whether potential barriers to entry, such as restrictions on starting a new
business, could affect the development of total alternative credit. These barriers to entry can be
seen as both demand side and supply side drivers. In particular, easier procedures to start a
business may allow new firms – such as those that sell products on e-commerce platforms and
use fintech or big tech credit – to enter the market, thus increasing the demand for credit. Easier
procedures may also allow fintech and big tech intermediaries to emerge, or foreign firms to
enter these markets, thus increasing the fintech and big tech credit supply.
In Table 3, we add to the baseline specification a number of indicators that measure how
easy is to open a new business. Each indicator ranges from 0 (difficult to open a business) to
10 (maximum ease). In particular, we consider one at a time (to avoid collinearity
problems) the following measures: (i) the overall score for the ease to start business; (ii) a
specific score based on the median duration to complete the procedure for creation of a firm;
(iii) a specific score based on the minimum capital required for an entrepreneur to start up and
formally operate a business; and (iv) a specific score based on overall costs officially required
for an entrepreneur to start up and formally operate an industrial or commercial business. In
2013 and 2014, these indicators are available only for 65 of the 79 countries in the sample and
therefore the number of observations drops from 453 to 425.
The results indicate that total alternative credit is positively correlated with all the
indicators that measure the ease to start new business. To get an indication of the
quantitative effect, we can observe countries in different quartiles of the overall score
distribution. For example, the difference in total alternative credit per capita between those
countries in the first quartile of the distribution (where it is relatively difficult to create a new
business) and countries in the last quartile (where it is relatively easier to create a new
business) is between 1 and 16%, depending on the specification.
19 Beyond the focus of this paper, institutional factors and social arrangements may influence not only the volume of
alternative credit, but also the form that such credit takes. See Wardrop (2020).
Drivers of total alternative credit – ease of doing business indicators
All variables are expressed in current USD, except where indicated Table 3
Ln(total alternative credit per capita)
GDP per capita1 ***
()
***
()
***
()
***
()
GDP per capita^2 *** *** *** ***
() () () ()
Lerner index *** *** *** ***
() () () ()
Bank branches per 100,000 *** *** *** ***
adult population () () () ()
Normalised regulation index3 *** *** *** ***
() () () ()
Score starting a business **
(overall) ()
Score-Time (days) **
()
Score-Paid-in Minimum capital **
(% of income per capita) ()
Score-Cost (% of income per *
capita) ()
Other controls4 Yes Yes Yes Yes
Geographic area fixed effects5 Yes Yes Yes Yes
No. of observations6 425 425 425 425
Estimation method OLS OLS OLS OLS
R2
Estimation period 2013–18. Robust standard errors in parentheses. ***/**/* denotes results significant at the 1/5/10% level. Ln = natural logarithm.
The dependent variable has been winsorised at the 1% and 99% level.
1 GDP per capita, in USD thousands. 2 Lerner index of banking sector mark-ups in economy i, reflecting market power by incumbent banks. 3
The index is normalised between 0 (no regulation) and 1 (max regulation). 4 Other controls include: GDP growth; a crisis dummy that takes the value of 1
if the country was hit by the GFC and 0 elsewhere; total banking credit growth to the private non–financial sector; mobile phones per 100 persons; a
dummy that takes the value of 1 for advanced economies and 0 elsewhere; and country-specific real interest rates. 5 The sample has been divided into
five geographical areas: Africa, Asia Pacific, Europe, Latin America, Middle East and North America.
Sources: CCAF; IMF, World Economic Outlook; World Bank; authors’ calculations.
In Table 4, we analyse how the development of total alternative credit per capita depends
on investor protection disclosure and efficiency of the judicial system. Higher investor
protection may make it easier to set up a new lending platform and to find investors.
Superior contract enforcement frameworks may limit credit risk and thus make lending more
attractive. Both are, thus, supply side factors. Again, to avoid multicollinearity
problems, we add one indicator at a time. The first column includes an indicator for the
business extent of disclosure (the extent to which investors are protected through
disclosure of ownership and financial information). The index ranges
from 0 to 10, with higher values indicating more disclosure. The other indicators take into
account the efficiency of the judicial system and the strength of insolvency resolution.
All indicators are taken from the World Bank Ease of Doing Business database.
Drivers of total alternative credit – investor protection and judicial system
All variables are expressed in current USD, except where indicated Table 4
Ln(total alternative credit per capita)
GDP per capita1 ***
()
***
()
***
()
***
()
GDP per capita^2 *** *** *** ***
() () () ()
Lerner index *** *** *** ***
() () () ()
Bank branches per 100,000 *** *** *** ***
adult population () () () ()
Normalised regulation index3 *** *** *** ***
() () () ()
Extent of disclosure index (0-10) *
()
Trial and judgment (days) ***
()
Enforcement of judgment **
(days) ()
Enforcement fees (% of claim) *
()
Other controls4 Yes Yes Yes Yes
Geographic area fixed effects5 Yes Yes Yes Yes
No. of observations 425 425 425 425
Estimation method OLS OLS OLS OLS
R2
Estimation period 2013–18. Robust standard errors in parentheses. ***/**/* denotes results significant at the 1/5/10% level. Ln = natural logarithm.
The dependent variable has been winsorised at the 1% and 99% level.
1 GDP per capita, in USD thousands. 2 Lerner index of banking sector mark-ups in economy i, reflecting market power by incumbent banks. 3
The index is normalised between 0 (no regulation) and 1 (max regulation). 4 Other controls include: GDP growth; a crisis dummy that takes the value of 1
if the country was hit by the GFC and 0 elsewhere; total banking credit growth to the private non–financial sector; mobile phones per 100 persons; a
dummy that takes the value of 1 for advanced economies and 0 elsewhere; and country-specific real interest rates. 5 The sample has been divided into five
geographical areas: Africa, Asia Pacific, Europe, Latin America, Middle East and North America.
Sources: CCAF; IMF, World Economic Outlook; World Bank; authors’ calculations.
Countries with more disclosure and stronger judicial systems have more developed
alternative forms of credit. The latter is higher where investors are protected through laws that
allow for higher disclosure of ownership and financial information. The sum of
fintech and big tech credit per capita is also larger in countries with: a lower average number
of days to complete a trial/judgment; a lower number of days to enforce the law; and lower
judicial enforcement fees.
Table 5 considers the development of total alternative credit in relation to country
characteristics of the banking We find that total alternative credit is less
developed in countries where the banking system supplies a larger amount of credit relative
to their deposit capacity (loan-to-deposit ratio). The correlation between total alternative credit
per capita and the overall bank loan-to-deposit ratio is negative and significant. From a
quantitative point of view, the difference in total alternative credit per capita between those
countries in the first quartile of the distribution (where bank loans to deposits are low) and
countries in the last quartile (where bank loans to deposits are high) is around 4%.
By contrast, total alternative credit is more developed where banks have a higher level of
capital. The second column shows a positive and significant correlation between the average
level of the bank regulatory capital-to-risk-weighted assets ratio and the amount of total
fintech credit per capita. This correlation is interesting because the relationship between
bank capital and bank risk aversion is uncertain a priori (Gambacorta and Mistrulli,
2004). If a higher level of bank capital reflects a higher level of risk aversion (Flannery, 1989;
Dewatripont and Tirole, 1994), a larger volume of fintech credit may reflect lending to risky,
potentially unbanked borrowers. If well-capitalised banks are more risk-averse, they could
select a pool of borrowers ex ante who are on average less financially fragile, thus containing
banks’ exposure to default risk when an economic downturn occurs. Interestingly, alternative
credit is also higher in countries with a higher level of bank provision to non-performing loans.
This could indicate a role for fintech and big tech credit in economies where the banking system
is more cautious or is more constrained by recent credit losses.
The fourth column includes an additional indicator for the presence of foreign banks:
outstanding loans from non-resident banks to GDP in each country. The result indicates that
total alternative credit is more developed when the level of foreign presence is low. This
result could indicate larger fintech and big tech credit volumes in those countries with lower
competition in the credit market, and thus corroborates the finding obtained using the Lerner
index of banking sector mark-ups.
In a last regression we include an indicator for the share of firms with access to
transaction accounts. This indicator is very important for developing economies and tends to
reduce the significance of the GDP per capita indicator. It shows a strong positive
correlation, implying more credit where a larger share of firms uses a formal bank account or
other formal transaction account services. This underscores that fintech and big tech credit may
complement bank credit to firms, as such alternative credit is higher in those economies where
firms are likely to have access to banking services.
20 The lower number of observations compared with Table 2 is due to the lack of data for the additional independent
variables used. Data are available for 65 countries out of 68 and only for 2015–17.
Drivers of total alternative credit – banking sector characteristics
All variables are expressed in current USD, except where indicated Table 5
Ln(total alternative credit per capita)
GDP per capita1 ***
()
***
()
***
()
***
()
*
()
GDP per capita^2 *** *** *** ***
() () () () ()
Lerner index *** ** ** *** ***
() () () () ()
Bank branches per 100,000 ** * ** ***
adult population () () () () ()
Normalised regulation index3 *** *** *** *** ***
() () () () ()
Bank credit to bank deposits **
(%) ()
Bank regulatory capital to risk- ***
weighted assets (%) ()
Provisions to non-performing ***
loans (%) ()
Loans from non-resident banks
to GDP (%)
**
()
Proportion of firms with a ***
transactions account (%) ()
Other controls4 Yes Yes Yes Yes Yes
Geographic area fixed effects5 Yes Yes Yes Yes Yes
No. of observations 212 197 187 194 276
Estimation method OLS OLS OLS OLS OLS
R2
Estimation period 2013–18. Robust standard errors in parentheses. ***/**/* denotes results significant at the 1/5/10% level. Ln = natural logarithm.
The dependent variable has been winsorised at the 1% and 99% level.
1 GDP per capita, in USD thousands. 2 Lerner index of banking sector mark-ups in economy i, reflecting market power by incumbent banks. 3
The index is normalised between 0 (no regulation) and 1 (max regulation). 4 Other controls include: GDP growth; a crisis dummy that takes the value of
1 if the country was hit by the GFC and 0 elsewhere (post-2006); total banking credit growth to the private non– financial sector; mobile phones per
100 persons; a dummy that takes the value of 1 for advanced economies and 0 elsewhere; and country- specific real interest rates. 5 The sample has
been divided into five geographical areas: Africa, Asia Pacific, Europe, Latin America, Middle East and North America.
Sources: CCAF; IMF, World Economic Outlook; World Bank; authors’ calculations.
Drivers of total alternative credit – financial market development
All variables are expressed in current USD, except where indicated Table 6
Ln(total alternative credit per capita)
GDP per capita1 *** *** *** *** ***
() () () () () ()
GDP per capita^2 *** *** *** ** *** ***
() () () () () ()
Lerner index *** * **
() () () () () ()
Bank branches per 100,000 ** * *
adult population () () () () () ()
Normalised regulation index3 *** *** *** *** *** ***
() () () () () ()
Corporate bond average **
maturity (years) ()
Corporate bond issuance **
volume to GDP (%) ()
Total factoring volume to GDP
(%) ()
Global leasing volume to GDP ***
(%) ()
Stock market total value traded ***
to GDP (%) ()
Stock market turnover ratio (%) **
()
Other controls4 Yes Yes Yes Yes Yes Yes
Geographic area fixed effects5 Yes Yes Yes Yes Yes Yes
No. of observations 132 137 145 78 163 161
Estimation method OLS OLS OLS OLS OLS OLS
R2
Estimation period 2013–18. Robust standard errors in parentheses. ***/**/* denotes results significant at the 1/5/10% level. Ln = natural logarithm.
The dependent variable has been winsorised at the 1% and 99% level.
1 GDP per capita, in USD thousands. 2 Lerner index of banking sector mark-ups in economy i, reflecting market power by incumbent banks. 3
The index is normalised between 0 (no regulation) and 1 (max regulation). 4 Other controls include: GDP growth; a crisis dummy that takes the value of
1 if the country was hit by the GFC and 0 elsewhere; total banking credit growth to the private non–financial sector; mobile phones per 100 persons; a
dummy that takes the value of 1 for advanced economies and 0 elsewhere; and country-specific real interest rates. 5 The sample has been divided into
five geographical areas: Africa, Asia Pacific, Europe, Latin America, Middle East and North America.
Sources: CCAF; IMF, World Economic Outlook; World Bank; authors’ calculations.
Finally, in Table 6, we show that total alternative credit is positively correlated with
indicators of development of the bond and equity market, and with other forms of non- bank
credit: factoring and leasing. Our hypothesis is that that alternative credit tends to
emerge alongside stock and bond markets, factoring and leasing, . that they are broadly
complements. In this case, data are available for a lower number of countries (42 to 49
depending on the specification) and for 2015–18 only. Despite the more limited sample,
overall, total alternative credit shows a statistically significant correlation with corporate bond
maturity and issuance volumes, total factoring and leasing volumes and stock market value and
turnover.
While fintech and big tech credit often serve smaller (. SME) corporate
borrowers, and individuals, volumes do correlate with market financing for larger firms. Further
tests (not reported) show that total alternative credit shows a strong positive association with
venture capital, private equity and merger and acquisition activity and the number of such
deals, from PitchBook Data. Thus, alternative credit seems to complement these other forms
of finance, not to substitute for them. This is consistent with other work on how alternative
finance and capital market financing can reach underserved borrowers, particularly SMEs
(World Bank, 2020).
4. Conclusion
This paper has documented the recent growth of fintech credit, as provided by non- bank
online platforms, and big tech credit, as provided by large companies whose primary
business is technology, sometimes in partnership with traditional financial institutions.
Based on data collected from the CCAF surveys, public sources and contacts with firms and
central banks, we have shown that both forms of credit have risen dramatically since 2013,
but that since 2018, big tech credit has overtaken fintech credit in total size. Based on
preliminary data, the shift to big tech credit was likely to have been even more pronounced in
2019.
We have assessed the economic and institutional factors driving the growth and adoption
of fintech and big tech credit. We find that fintech and big tech credit are more developed with
higher GDP per capita, but at a declining rate. We also find that these alternative forms of
credit are larger where banking mark-ups are higher and where banking regulation is less
stringent. Regulation and, to a lesser extent, mark-ups are particularly important for fintech
credit. Fintech credit is also more prevalent where there are fewer bank branches per capita. We
also find that fintech and big tech credit are more developed where 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. Overall, these alternative forms of credit seem to complement more traditional
credit markets, rather than substitute for them.
It is of course difficult to foresee how large fintech and big tech credit will become in the
future. Given accommodative supply side and demand side drivers in some economies,
fintech and big tech credit are growing to sizes that could be relevant to financial stability. In
specific markets, for instance small businesses lending in the United Kingdom and China, and
consumer lending in Kenya, fintech and big tech lending have a significant market share.
Some big tech players (particularly in China) have likely reached a level of systemic
importance. Moreover, as credit has grown rapidly, there is the potential for individual
borrowers to become overindebted, and – as in past periods of rapid credit growth – even for
risks to financial stability. Whether this growth represents the natural diffusion of a
promising new type of intermediation or a credit
bubble remains an open question; it may be possible to assess this only after the economy
has undergone stressed conditions.
In this light, the current economic shock from the Covid-19 pandemic represents a test for
fintech and big tech credit – and could have a large impact on both types of lending going
forward. First, on a very basic level, the reduced physical mobility and social distancing
during the pandemic have encouraged online communication channels. Thus, the use of
online interfaces to originate and service loans could make both fintech and big tech lending
more attractive relative to brick-and-mortar financial institutions. Second, the pandemic has
benefited e-commerce and online economic activities, while hurting activities that rely on
physical location. This may accelerate certain more general trends toward digitalisation, and
may even give big tech firms greater margins in their core businesses (e-commerce,
social media, advertising, gaming, telecommunication etc.). Finally, the shock marks the first
economic downturn that many fintech and big tech lenders have undergone, and it is thus a
test of the resilience of their credit scoring models under stress. Evidence from a 2017
regulatory shock in China (Gambacorta et al., 2019) suggests that models with alternative data
and machine learning may outperform traditional data in predicting distress after a shock. Yet
it is also plausible that machine learning models that use short periods of historical data may
perform worse, or that this may differ across different markets. Big tech firms, which have a
more diversified range of business lines and fund loans either through their own balance
sheet or financial institution partners, may have greater capacity to absorb credit losses and
continue lending than fintech credit platforms, which are focused solely on financial services
and must continue appealing to investors. Big tech credit may also be less correlated with
collateral values (Gambacorta et al., 2020).
Regardless of the impact of the current shock, there is a need to ensure that
authorities and researchers have the proper data to monitor and study fintech and big tech
credit platforms. Authorities with a mandate for guaranteeing financial stability should not
have to “fly blind” or rely exclusively on non-official data sources; they should have access to
timely and accurate information about fintech and big tech credit in their own economy and
economies around the world. We hope with this paper to make a small contribution toward
this goal. Complementary efforts to bring fintech and big tech lenders into the fold of official
regulatory reporting should continue apace.
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Annex A: Methodological notes on database construction
This annex gives a brief overview of methodological choices in the construction of the
database.
Fintech credit in 2019: CCAF survey data for 2019 are not yet available. As such,
2019 volumes have been estimated based on the 2018 CCAF survey and more recent
data. For instance, the 2019 estimate for fintech credit in China is based on
information from the People’s Bank of China, while the estimate for Indonesia comes
from Bank Indonesia. For these countries, it is assumed that the ratio between the
2018 CCAF survey data and the 2018 estimates for these countries remains constant in
2019, to arrive at a projected 2019 CCAF survey estimate. For India, we use input
from a sample study by Reserve Bank of India for total lending on digital platforms in
2019. For the US, UK, European Union, Australia and New Zealand, estimates have
used platform-level information from Brismo where available, and the median
growth rate for remaining platforms. For Korea, we have used data from the
Korea P2P Lending Association, kindly translated by Bank of Korea. Together, these
countries made up % of global fintech credit volumes in 2018. Data for other
markets (. the remaining % of volumes) were extrapolated based on the median
growth rate in 2017-2018.
Converting stocks to flows (and vice versa): In most cases, fintech credit
platforms and big tech firms report their cumulative lending flow over a
calendar year or since the inception of their business (if they report at all). A
smaller number report the stock of loans outstanding at the end of a year. In the
latter case, it is necessary to convert the credit stock to a lending flow. To do this, we
take the difference between end-year stocks (. at end-2017 and end-2018), and add
to this the volume of loans that are assumed to have matured over the calendar
year. This can be estimated by the stock of loans at the start of the year divided by the
average maturity. To estimate the stock of fintech credit, we do the reverse
procedure, adding the lending flow minus matured loans to the stock of lending
from the end of the previous year, assuming an average maturity of 9 months. To
estimate the stock of big tech credit, we take the ratio of credit stocks (where
available, and excluding China) to flows, and apply the same ratio to other
countries. For China, where loan maturities are shorter, we apply the same estimation
technique, but the ratio is calculated only on Chinese big tech firms. For African
countries, where loan maturities are generally much shorter, we assume an average
maturity of one month (based on input from company contacts).
Lerner Index: The World Bank Lerner Index of banking sector mark-ups is
available only through 2014. To fill in the values for 2015–17, we perform a simple
OLS regression of the World Bank data for 2011–14 against the the alternative
cyclical measure devolped by Igan et al. (2020). We then use the predicted (fitted)
values for 2015–17. Outliers (Greece and Guatemala) were winsorised at the 99%
level.
The big tech and fintech credit dataset is available along with the paper.
Annex B: Robustness checks
Drivers of fintech and big tech credit volumes: cross-section analysis
All variables are expressed in current USD, except where indicated Table B1
credit)
Estimation period 2013–18. Robust standard errors in parentheses. ***/**/* denotes results significant at the 1/5/10% level. Ln = natural logarithm.
The dependent variables have been winsorised at the 1% and 99% level.
1 GDP per capita, in USD thousands. 2 Lerner index of banking sector mark-ups in economy i, reflecting market power by incumbent banks. 3
The index is normalised between 0 (no regulation) and 1 (max regulation). 4 Other controls include: GDP growth; a crisis dummy that takes the value of 1
if the country was hit by the GFC and 0 elsewhere; total banking credit growth to the private non–financial sector; mobile phones per 100 persons; and
country-specific real interest rates. 5 Big tech credit is zero in 47 countries. To allow the computation of the log of the ratio (not defined for zero), big tech
credit has been rescaled summing an arbitrary constant (the minimum value). 6 Fintech credit is defined as credit activity facilitated by electronic
platforms that are not operated by commercial banks or big tech firms. 7 The sample has been divided into five geographical areas: Africa, Asia Pacific,
Europe, Latin America, Middle East and North America.
Sources: CCAF, authors’ calculations.
Ln(total alternative
Ln(big tech credit5) Ln(fintech credit6) Difference │b-a│
(a) (b) H0: │b-a│<0
GDP per capita1 *** * *** **
() () () ()
GDP per capita^2 *** ** **
() () () ()
Lerner index2
() () () ()
Bank branches per 100,000 *
adult population () () () ()
Normalised regulation index3 *** *** ***
() () () ()
Other controls4 Yes Yes Yes
Geographic area fixed effects7 Yes Yes Yes
No. of observations 79 79 79
Estimation method OLS OLS OLS
R2
Drivers of fintech and big tech credit volumes (region and year fixed effects)
All variables are expressed in current USD, except where indicated Table B2
credit)
Estimation period 2013–18. Robust standard errors in parentheses. ***/**/* denotes results significant at the 1/5/10% level. Ln = natural logarithm.
The dependent variables have been winsorised at the 1% and 99% level.
1 GDP per capita, in USD thousands. 2 Lerner index of banking sector mark-ups in economy i, reflecting market power by incumbent banks. 3
The index is normalised between 0 (no regulation) and 1 (max regulation). 4 Other controls include: GDP growth; a crisis dummy that takes the value of 1
if the country was hit by the GFC and 0 elsewhere; total banking credit growth to the private non–financial sector; mobile phones per 100 persons; and
country-specific real interest rates. 5 Big tech credit is zero in 47 countries. To allow the computation of the log of the ratio (not defined for zero), big tech
credit has been rescaled summing an arbitrary constant (the minimum value). 6 Fintech credit is defined as credit activity facilitated by electronic
platforms that are not operated by commercial banks or big tech firms. 7 The sample has been divided into five geographical areas: Africa, Asia Pacific,
Europe, Latin America, Middle East and North America.
Sources: CCAF, authors’ calculations.
Ln(total alternative
Ln(big tech credit5) Ln(fintech credit6) Difference │b-a│
(a) (b) H0: │b-a│<0
GDP per capita1 *** *** *** ***
() () () ()
GDP per capita^2 *** *** *** ***
() () () ()
Lerner index2 *** ** *** *
() () () ()
Bank branches per 100,000 *** ** ***
adult population () () () ()
Normalised regulation index3 *** *** *** ***
() () () ()
Other controls4 Yes Yes Yes
Geographic area fixed effects7 Yes Yes Yes
Year fixed effects Yes Yes Yes
No. of observations 453 453 453
Estimation method OLS OLS OLS
R2
Drivers of bank credit volumes
All variables are expressed in current USD, except where indicated Table B3
Ln(bank credit) Ln(bank credit)
Baseline Cross section
R2
Estimation period 2013–18. Robust standard errors in parentheses. ***/**/* denotes results significant at the 1/5/10% level. Ln = natural logarithm.
The dependent variable has been winsorised at the 1% and 99% level.
1 GDP per capita, in USD thousands. 2 Lerner index of banking sector mark-ups in economy i, reflecting market power by incumbent banks. 3
The index is normalised between 0 (no regulation) and 1 (max regulation). 4 Other controls include: GDP growth; a crisis dummy that takes the value of 1
if the country was hit by the GFC and 0 elsewhere;; mobile phones per 100 persons; dummy that takes the value of 1 for advanced economies and 0
elsewhere; and country-specific real interest rates. 5 The sample has been divided into five geographical areas: Africa, Asia Pacific, Europe, Latin
America, Middle East and North America.
Sources: CCAF, authors’ calculations.
Ln(GDP per capita)1 ***
()
***
()
Ln(GDP per capita)^2 ***
()
***
()
Lerner index2 **
() ()
Bank branches per 100,000 *** **
adult population () ()
Normalised regulation index3 ***
()
***
()
Other controls4 Yes Yes
Geographic area fixed effects5 Yes Yes
No. of observations 453 79
Estimation method OLS OLS
Drivers of fintech and big tech credit volumes
All variables are expressed in current USD, except where indicated Table B4
Ln(total alternative
credit per capita)
Ln(big tech credit per
capita5)
Ln(fintech credit per
capita6)
Difference │b-a│
(a) (b) H0: │b-a│<0
GDP per capita1 ***
()
***
()
***
()
**
()
GDP per capita^2 *** *** ***
() () () ()
Lerner index2 *** ** *** **
() () () ()
Bank branches per 100,000 *** *** ***
adult population () () () ()
Normalised (banking) *** *** *** ***
regulation index3 () () () ()
Explicit fintech credit regulation *** *** ***
dummy () () () ()
Other controls4 Yes Yes Yes
Geographic area fixed effects7 Yes Yes Yes
No. of observations 453 453 453
Estimation method OLS OLS OLS
R2
Estimation period 2013–18. Robust standard errors in parentheses. ***/**/* denotes results significant at the 1/5/10% level. Ln = natural logarithm.
The dependent variables have been winsorised at the 1% and 99% level.
1 GDP per capita (in USD thousands). 2 Lerner index of banking sector mark-ups in economy i, reflecting market power by incumbent banks. 3
The index is normalised between 0 (no regulation) and 1 (max regulation). 4 Other controls include: GDP growth; a crisis dummy that takes the value of
1 if the country was hit by the GFC and 0 elsewhere; total banking credit growth to the private non–financial sector; mobile phones per 100 persons; a
dummy that takes the value of 1 for advanced economies and 0 elsewhere; and country-specific real interest rates. 5 Big tech credit is zero in 47 countries.
To allow the computation of the log of the ratio (not defined for zero), big tech credit has been rescaled summing an arbitrary constant (the minimum
value). 6 Fintech credit is defined as credit activity facilitated by electronic platforms that are not operated by commercial banks or big tech firms. 7
The sample has been divided into five geographical areas: Africa, Asia Pacific, Europe, Latin America, Middle East and North America.
Sources: Rau (2020); CCAF; IMF, World Economic Outlook; World Bank; authors’ calculations.
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