Diginomics WORKING PAPER - The impact of fintech startups on financial institutions' performance and default risk - Uni Bremen

Diginomics
WORKING
PAPER

The impact of fintech startups on financial
institutions’ performance and default risk
Christian Haddad and Lars Hornuf

 April 2021 No. 0004

Impressum:

Diginomics Working Paper

ISSN: 2701-6307

Published by:
Universität Bremen, Diginomics Research Group, Max-von-Laue-Straße 1, 28359 Bremen,
Germany

Editor:
Prof. Dr. Lars Hornuf
Phone: +49(0)421 218 66820
E-mail: hornuf@uni-bremen.de

https://www.uni-bremen.de/graduiertengruppe-diginomics/forschung/diginomics-working-
paper

The impact of fintech startups on financial institutions’
 performance and default risk

 Christian Haddad
 Excelia Business School
102 Rue de Coureilles - Les Minimes | 17024 La Rochelle, France
 Phone: +33-(0)5 16 19 6394
 Email: haddadc@excelia-group.com

 Lars Hornuf
University of Bremen, Faculty of Business Studies and Economics
 Max-von-Laue-Straße 1 | 28359 Bremen, Germany
 Phone: +49-421-218-66820
 Email: hornuf@uni-bremen.de

 Max Planck Institute for Innovation and Competition
 Marstallplatz 1 | 80539 Munich, Germany
 Email: lars.hornuf@ip.mpg.de

 CESifo
 Poschingerstraße 5 | 81679 Munich, Germany

 1

The impact of fintech startups on financial institutions’
 performance and default risk

 Abstract
 We study the impact fintech startups have on the performance and the default risk of
 traditional financial institutions. We find a positive relationship between fintech
 startup formations and incumbent institutions’ performance for the period from 2005
 to 2018 and a large sample of financial institutions from 87 countries. We further
 analyze the link between fintech startup formations and the default risk of traditional
 financial institutions. Fintech startup formations decreases stock return volatility of
 incumbent institutions and decreases the systemic risk exposure of financial
 institutions. Our findings indicate that the development of fintech startups should be
 monitored very closely by legislators and financial supervisory authorities, because
 fintechs not only have a positive effect on the financial sector’s performance, but can
 also improve financial stability relative to the status quo.

 This version: April 24, 2021

JEL Classification: K00, L26, O3

Keywords: Fintech, Bank Performance, Default Risk, Financial Stability

 2

1. Introduction

The rise of financial technology (fintech) has received considerable attention from academics,

practitioners, and regulators. The recent hype about fintech results from the development and

deployment of novel technologies such as artificial intelligence (AI), big data, cloud computing,

machine learning, blockchain and other technologies that have the potential to revolutionize the

financial sector, which was historically considered to be among the most traditional and

conservative sectors in the economy. The Financial Stability Board of the Bank for International

Settlements defines fintech as “(…) as technology-enabled innovation in financial services that

could result in new business models, applications, processes or products with an associated

material effect on the provision of financial services” (European Banking Authority, 2017, p. 7).

Fintech innovations have emerged in many of the traditional value-adding areas of a universal

bank, which include financing, asset management, payment services, and other activities

(Dorfleitner et al., 2017). Fintech startups not only challenge traditional financial institutions by

providing cheaper, faster, and easier access to financial services, but potentially also foster the

transformation and innovation activities of incumbent institutions (An and Rau, 2019; Di, et al;

2020; Milian et al., 2019; Panos and Wilson, 2020). Until today, little is known about the way

fintech startups affect the traditional financial sector.

One of the core functions of financial institutions is the intermediation of financial resources

(Merton, 1992). Yet, the financial crisis of 2007–2008 created a credit crunch (Cowling et al., 2012;

Campello et al., 2010; Campello et al., 2011), which resulted in many small and medium size firms

being financially constrained (Mc Cahery et al., 2015). Economic output declined sharply and

unemployment rates increased worldwide (Bruno et al., 2014; Daly et al., 2012). Moreover, bank

customers lost their confidence in many of the traditional financial institution, which in some

 3

regions resulted in bank runs like in the case of British Northern Rock in 2008. This fragile

environment provided the ground for novel products and services of fintech startups, which started

with a clean slate and did not have to overcome a history of failure and excessive risk taking. As a

result of consumers’ distrust in banks, marketplace lending has become more prevalent (Saiedi et

al., 2020). In particular, banks’ misconduct has been shown to be related to the emergence of the

United States (US) online lending market (Bertsch et al., 2020). Moreover, recent research indicate

that fintech startups have the potential to better address information asymmetries (Ge et al., 2017;

Lin et al., 2013; Xu et al., 2018), because they leverage additional information about borrowers

form the Internet, thereby enabling them to receive credit for the first time or in some cases at

cheaper rates (Jagtiani and Lemieux, 2019; Iyer et al., 2016; Serrano-Cinca et al., 2015). Services

provided by fintech startups also include, for example, algorithm-based investment advice, mobile

banking services, instant online and mobile payment infrastructure, innovative risk management

systems, and cost-efficient foreign exchange services (Haddad und Hornuf, 2019). The increasing

prevalence of fintech startups and the potential pressure they exercise on incumbent firms raise the

question how fintech startups affect the performance and risk taking of traditional financial

institutions.

It has often been argued that fintech startups do not fully comply with financial regulation and

engage in regulatory arbitrage using existing legal exemptions (Buchak et al. 2018; Cumming and

Schwienbacher, 2018; Hornuf and Schwienbacher, 2017), which might consequently undermine

financial stability (Vučinić, 2020; Fung et al, 2020; Li et al., 2020). Because financial stability has

a direct impact on economic growth, scholars have investigated financial institutions’ default risk

and under which conditions this risk led to subsequent financial turmoil (Acharya and Naqvi, 2012;

Beck et al., 2006; Diamond and Rajan, 2012). Fintechs can, for example, affect systemic risk

 4

through their increasing interconnectedness with traditional financial institutions and a lax

supervision by the authorities (BIS, 2017). By contrast, new business models that are uncorrelated

with traditional financial institutions can also reduce systemic risk in the financial industry. To the

best of our knowledge, empirical research has not yet studied the systemic risk of traditional

financial institutions resulting from fintech startup formations. In this article, we therefore raise the

question: Does the emergence of fintech startups affect systemic risk of traditional financial

institutions?

The empirical literature on financial innovation in general and interaction between traditional

financial institutions and fintechs in particular is still scarce. Lerner (2002) and Miller (1986)

measure financial innovation by the filing of financial patents and evidence that it has been

increasing from the late 1970s onwards. The quality of financial patents and financial innovations

was, however, often perceived to be low (Lerner et al. 2016). Research by Scott et al. (2017) shows

that the financial industry traditionally spent a large share of their expenses on IT, reaching around

one-third of all expenses in the early 1990s. In particular, the financial industry employed

computers early on. However, there have only been few financial innovations—e.g., the automated

teller machine—that have led to considerable changes in financial institutions and their business

models (Merton 1995). Whether fintechs affects incumbents’ ability to innovate and consequently

performance is still an open question.

A related article to ours focusing on bank–fintech alliances is that of Brandl and Hornuf (2020),

who run a simply bank–fintech network analysis for Germany and find that bank–fintech

relationships are often product-related. They argue that this form of alliance results from that fact

that most fintechs develop an algorithm or software, the value of which can only be determined

when the software has been adapted more thoroughly to customer needs. Hornuf et al. (2020) refine

 5

these findings by analyzing bank characteristics that are associated with a bank–fintech alliance.

They hand-collected data for the largest banks from Canada, France, Germany, and the United

Kingdom, and provide detailed evidence that banks are more likely to form alliances with fintechs

when they pursue a well-defined digital strategy and/or employ a chief digital officer. Furthermore,

they find that banks more often invest in small fintechs but often build product-related

collaborations with larger fintechs, which is in line with predictions from incomplete contract

theory (Aghion and Bolton 1992; Grossman and Hart 1986). Phan et al. (2020) investigate a sample

of 41 Indonesian banks. They find that fintech negatively predicts bank performance and argue that

fintechs substitute traditional banks by providing less expensive and more efficient services

In this article, we collected data for 8,092 financial institutions and 12,549 fintech startups from 87

countries to study the effect fintech startups have on the performance and default risk of traditional

financial institutions. Our results indicate a positive and significant impact of fintech formations

on financial institutions’ performance. An increase of fintech startup formations are associated with

an increase of incumbent institutions’ performance. Our findings also suggest that fintech

formations decrease stock return volatility and financial institutions’ exposure to systemic risk.

These findings might be of interest to academics, practitioners, and regulators alike, especially as

the fintech sector is steadily growing and becomes more and more integrated with the traditional

economy and incumbent financial institutions (Li et al., 2020).

The remainder of the article is organized as follows. Section 2 summarizes the literature and

introduces our hypotheses. Section 3 describes the data and introduce the variables used in the

quantitative analysis. Section 4 presents the descriptive and multivariate results. Section 5 provides

a discussion and conclusion of our study.

 6

2. Literature and Hypotheses

An extensive literature has investigated the performance of financial institutions. During the last

decade, research has studied the determinants of financial institutions performance analyzing how

firms address corporate governance issues (Aebi et al., 2012; Peni and Vähämaa, 2012; Zheng and

Das, 2018), master the diversification of their business activities (Berger et al., 2010; Brahmana et

al., 2018; Chen, et al., 2018; Kim et al., 2020), deal with external regulation (Naceur and Omran,

2011, Psillaki and Mamatzakis, 2017), react to monetary policies (Mamatzakis and Bermpei, 2016;

Gambacorta and Shin, 2018), deal with the legal and institutional framework (Kalyvas and

Mamatzakis, 2017; Bitar and Tarazi, 2019; El Ghoul et al., 2020), generate intellectual capital

(Adesina, 2021; Nawaz, 2019; Talavera, et al., 2018), and engage in shadow banking activities

(Tan, 2017; Lin et al., 2018). Given the all-embracing and massive development of the fintech

sector during the last decade, it appears worthwile to also investigate how fintech startup

formations affect financial institutions performance.

Consumer theory stipulates that a new product or service—such as those developed by fintech

startups—either act as complement or substitute to existing products or services (Aaker and Keller,

1990; Frank, 2009). The products and services that fintech startups offer are more likely to benefit

traditional financial institutions if they constitute complements, but threaten incumbent

institutions’ performance if they are substitutes (Kaul, 2012). While fintechs have the potential to

develop revolutionary business models, collaborations between banks and fintechs have most often

been evolutionary in nature (Bhalla, 2019). Thus, existing products or services have been merely

enhanced and innovations did rarely replace existing ones (Merton, 1995). For example, invoice

trading and factoring always existed but the innovation of fintechs was to scale these services down

and offer them for small and medium-sized enterprises (Dorfleitner et al. 2017).

 7

Most research concludes that information technology is beneficial for incumbent institutions,

because it helps to reduce transaction costs, thereby improving service quality, optimizing business

structure, and promoting business transformation and upgrading (Lapavitsas and Dos Santos, 2008;

Martin-Olivier and Salas-Fumas, 2008; Shu and Strassmann, 2005). Moreover, there is empirical

evidence that many incumbent institutions acknowledge the superiority of fintech startups and have

incorporated these start-ups and/or their products and services into their own business model

(Hornuf et al. 2020). For these financial institutions, the emergence of fintech startups results in a

beneficial partnership rather than a threat (PwC, 2016). For example, the verification of customers'

identity through account or video verification supports the customer onboarding process, without

cannibalizing existing business from incumbents.

Historically, some scholars have claimed that the opposite is true and information technology could

bring enormous challenges to commercial banks (Holland et al., 1997), because information

technology, globalization, and deregulation allow for new market entrants, disintermediation,

innovation and customer changes on a massive scale. Accordingly, fintech startups would take over

several key functions of traditional financial institutions (Li et al., 2017). New market entrants

benefit from their lack of legacy infrastructure and low levels of organizational complexity, which

allows them to be more agile, innovate faster, and be more radical in their approach to innovation

(Brandl and Hornuf, 2020). Put differently, fintech startups are likely to absorb the existing

business of traditional financial institutions where the latter are operating inefficiently. This

substitution effect is also in line with disruptive theory (Christensen, 2013), which claims that new

entrants effectively compete with traditional players by providing accessible and cost-effective

goods and services to customers. As a result, startups eventually replace incumbents. Fintech have

already sparked such a disruptive evolution when offering financial products and services to

 8

customers in novel and more cost-effient ways (Ferrari, 2016). The efficiency increase due to

fintechs results, for example, from disintermediation that significantly lowers transaction costs for

consumers (PwC, 2016; KMPG, 2016). Blockchain technology is one of the most prominent

inventions that can accomplish such efficiency increases (Peters and Panayi, 2016; Wood and

Buchanen, 2015), for example, by making the clearing and settlement of securities and many other

services of the financial sector more cost effective.

Moreover, fintechs have developed applications to improve efficiency in financial services across

a range of other services, including, for example, mobile and instant payment services, automated

asset management, as well as digital information and data management (Villeroy de Galhau, 2016).

These innovations take advantage of the traditional financial institutions because many incumbants

still rely on outdated IT infrastucture (Laven and Bruggink, 2016; Brandl and Hornuf, 2020) and

have difficulties to adopt new financial products and services or in the same quality as fintechs.

Furthermore, traditional financial institutions are often less likely to adopt new technologies

quickly because of restrictions stemming from the regulatory environment that applies to fully

regulated institutions (Hannan and McDowell, 1984).

One might also argue that fintechs have no effect on banks performance, because fintechs attract

customers that traditional financial institutions do not serve. One of the most prominent examples

is probably the implementation of mobile payment and banking services in Kenya (Jack and Suri,

2014; Suri and Jack 2016). Moreover, Jagtiani and Lemieux (2018) find that consumer lending

activities on the platform LendingClub have penetrated areas that may be underserved by

traditional banks, mostly in highly concentrated markets and areas that have had fewer bank

branches. For example, risky startup firms and consumers that lack a credit history often do not

obtain access to credit, especially if the desired loan amounts are small and are associated with

 9

high transaction costs (Demos, 2016; Hayashi, 2016). Fintech startup often use novel, sometimes

algorithm-driven technology to assess borrowers’ creditworthiness at lower costs, which has been

an advantage over traditional banks that operate physical branches and employ human loan officers

(Hayashi, 2016).

Finally, existing financial institutions can acquire fintech startup that are perceived as “too”

innovative and cost effective. In this way, incumbents institutions gain access to new technology

and can adapt it to their own specific needs. For example, Capital One, one of the largest banks in

the US in terms of total assets and market capitalization, acquired the fintech startup Level Money

in 2015. Level Money was a San Francisco-based digital banking technology firm, which provided

customers with a simple overview of their finances. With more than 800,000 downloads, the Level

Money app connects to 250 US financial institutions (Li et al., 2017). After its acquisition by

Capital One, Level Money became part of Capital One’s Digital Innovation Team, which enables

Capital One to strengthen its capabilities in digital banking technologies.1

Because of fintech startups, financial institutions might not only acquire new retail customers, but

could also extend their existing business through fintech corporate clients. Some of the more

traditional financial institutions have realized the potential that stems from the emergence of fintech

statups and have specialized in what is called “banking as a service” (BaaS) or “banking as a

platform” (BaaP). In the BaaS business models financial institutions operate a licensed and

regulated banking back-end and offer BaaS middleware to fintech startups that cannot or do not

want to afford the costs of being fully regulated themselves. In other cases, financial institutions

might offer regulatory advice or technology to fintechs, which have not acquired the respective

1
 See: https://www.forbes.com/sites/peterhigh/2016/12/12/how-capital-one-became-a-leading-digital-
bank/?sh=581083b615ee

 10

knowledge or find it not cost-efficient to do so. In either case, the devision of value creation

between fintechs and banks might ultimately benefit both. Overall, we therefore conjecture that

financial institutions will not go down without a fight or without any attempt to improve their

business models after the emergence of fintech startups. We therefore hypothesize the following:

H1: Fintech startup formations are positively related to traditional financial institutions’

performance.

Extensive theoretical and empirical research has investigated the determinants of the default risk

of financial institutions, because financial stability is of utmost important for the economy and

financial supervisory authorities. Finance scholars have studied the default risk of financial

institutions mostly from two perspectives. The first stream of literature focuses on financial

institutions’ characteristics including their size (Afonso et al., 2015; Laeven and Levine 2009;

Saunders et al., 1990), liquidity (Diamond and Dybvid 2000; Diamond and Rajan 2012),

diversification of funding activities (Demirguc-Kunt and Huizinga 2009), bank capital as a share

of risk-weighted credit exposures (Furlong and Keely, 1989), and corporate governance (Agoraki,

et al., 2010; Chen et al; 2017). The second stream of literature focuses on the determinants of risk-

taking that results from external sources, such the degree of bank competition (Beck et al., 2006;

Beck et al., 2013, Boyd and De Nicolo 2005), monetary policy (Borio and Zhu 2012; Chen et al.,

2017), deposit insurance schemes (Angkinand and Wihlborg, 2010; Demirguc-Kunt and

Detragiache, 2002), external regulation (Barth et al., 2004; Klomp and Haan 2012), such as creditor

and minority shareholder protection (Houston et al., 2010; La Porta et al., 2000), and political

institutions (Ashraf 2017; Chen et al., 2015; Wang and Sui 2019).

 11

In this study, we investigate the default risk of financial institutions following the emergence of

fintech startups. Which impact fintechs have on the default risk of financial institutions is per se

not clear. Several factors could lead to an increase in the default risk in the financial industry.

Fintech startup often provide similar financial products and services as incumbents (Dorfleitner et

al., 2017; Yao et al., 2018; Kommel et al., 2019) and their business models are in some cases

inherently linked to traditional financial institutions. For example, in many jurisdictions

commercial loans can only be extended by institutions that possess a banking license. Marketplace

lending platforms, for example, often do not possess a banking license and a bank in the

background ultimately extends the loan between the borrower and the lenders (Cumming and

Hornuf, 2020). Thus, banks are often an integral part of fintech business models. However, startups

generally fail more often than established firms (Cressy, 2006; Evans, 1987; Dunne et al., 1989),

which could increase the risk of those that collaborate with them.

Buchak et al. (2018) provide empirical evidence for the US that the shadow bank market share in

residential mortgage origination almost doubled from 2007 to 2015. The increase in shadow banks

came with a dramatic growth in online fintech lenders, technological advantages, and regulatory

differences among US counties. In other cases, banks and fintechs cooperate closely because the

cooperation appears beneficial to all involved parties (Romānova and Kudinska, 2017, Hornuf et

al., 2020). As a result of these interconnections, the risks resulting from fintech formations could

spill over to individual financial institutions (European Banking Authority, 2017; He et al., 2017).

Moreover, banks themselves are actively involved and participate in the development of fintech

technology (Acar and Citak, 2019), which might result in increasing legal and technical risks, such

 12

as data security risk 2 , data privacy risk, and transaction risk, which could increase financial

institutions default risk (Cost of Data Breach Report, 2020; Yadron et al., 2014).

On the other hand, fintechs could also lower the default risk of financial institutions. The

digitalization of lending activities very likely lowers transaction costs and improves the efficiency

of the loan origination and maintenance processes (BIS, 2017). This could reduce the costs of

capital for borrowers and improve the risk-adjusted returns for fintechs and traditional financial

institutions. Moreover, because fintech startups employ modern technology and use big data, they

can at least theoretically better address information asymmetries (Ge et al., 2017; Lin et al., 2013;

Xu et al., 2018). Ecosystems that promote the sharing of data can further enable the development

of novel products and services. The European Banking Authority (2019) expects a positive effect

of application programming interfaces (APIs), which allow for a more direct exchange of data,

leading to increased competitive pressure, and an improved customer experience. We therefore

hypothesize the following:

H2a: Fintech startup formations decrease financial institutions’ default risk.

Traditional financial institutions invest in fintech startups, which allows them to better access their

knowledge (Lee and Shin, 2018, Hornuf et al., 2020). As fintechs grow larger and become more

integrated and interconnected with the financial sector, they may also impact systemic risk. A

prominent recent example is the German payment acquirer Wirecard, which in 2020 collapsed and

subsequently filed for default because of a series of fraudulent accounting activities and inflated

profits. Although Wirecard has been part of the Prime Standard, the market segment of the

Frankfurt Stock Exchange with the highest transparency standards, it was itself considered to be a

2
 See for example the massive data breach at JP Morgan, https://www.theguardian.com/business/2014/oct/02/jp-
morgan-76m-households-affected-data-breach.

 13

fintech company. Wirecard not only collaborated with other fintech startups such as Holvi,

Lendico, Number 26 (now N26), Rate Pay, and Zencap3, but also engaged in alliances with large

financial conglomerates such as insurance company Allianz.4 To offer lending services, Wirecard

operated the subsidiary Wirecard Bank, which possessed a banking license and was fully regulated

and monitored by the Federal Financial Supervisory Authority (Bundesanstalt für

Finanzdienstleistungsaufsicht – BaFin). Wirecard was not classified as a financial holding

company and only the subsidiary had been classified as a financial company by BaFin, which

implied that the holding company’s activities were supervised only loosely and accounting fraud

remained undetected (Navaretti et al. 2020).

Although bank–fintech collaborations have been rapidly growing in many economies, the related

supervision has developed only slowly, as the Wirecard case evidences. After the collapse of

Wirecard, the German legislator proposed a draft law to strengthen financial market integrity

(Finanzmarktintegritätsstärkungsgesetz – FISG) targeting a wide range of existing financial

market regulation. While the Wirecard accounting scandal did not affect the German or European

financial system as such, it as raised questions with regard to how financial subsidiaries of tech

companies can seamlessly continue operating after a holding company files for default and how

business partners can seamlessly switch their operations to another institution. Without doubt, as

fintechs become more mature and interconnected, concerns about market risk and systematic risk

rise. However, it should be mentioned that the collapse of Wirecard did by far not result in a

financial turmoil comparable to the collapse of Lehman Brothers in 2008.

3See http://ir.wirecard.de/download/companies/wirecard/Presentations/WDIInvestorPresentationQ22015_01.pdf
4
 See https://www.reuters.com/article/us-wirecard-accounts-allianz/allianz-to-end-wirecard-cooperation-amid-
accounting-scandal-idUKKBN2425KO

 14

Moreover, having access to alternative financial products such as marketplace or mobile loans,

which are to a lesser degree correlated with other loans and institutions, can reduce systemic risk

in the financial industry (BIS, 2017). A greater share of fintech credit through marketplace loans

or mobile loans could thus mitigate problems of too-big-to-fail or too-systemic-to-fail institutions.

Marketplace lending platforms operated by fintechs have little direct financial exposures to each

other, a systemic benefit that might only disappear if fintechs become more interconnected over

time (BIS, 2017). Furthermore, the use of biometric information and other enhanced data security

measures that have been implemented by fintechs early on are considered to improve data security,

potentially lowering the risk of cyber-attacks. Finally, systemic risk could also be reduced through

enhanced market transparency, which could result from the more extensive use of cloud computing

and decentralization (European Banking Authority, 2017). We therefore hypothesize the following:

H2b: The exposure of traditional financial institutions to systemic risk is negatively related to

fintech startup formations

3. Data and Method

3.1 Dependent variables

To investigate whether fintech startup formations affect incumbent institutions performance and

default risk, we consider eight dependent variables. For most of these variables, we need daily

stock returns as a basis. For financial institutions from the US, we obtained daily stock returns from

the Center for Research in Security Prices (CRSP) US Stock Databases and for all other countries

we used the CompuStat World Database. Because fintechs might not only affect the business

 15

models of banks but also other financial institutions, we extract 8,092 financial institutions5 from

87 countries with Standard Industrial Classification codes starting with 60 to 67 over the period

from 2005 to 2018 (see Appendix Table A1 for an overview). For each listed financial institution,

we collect adjusted prices or adjustment factors, the number of shares outstanding, the location of

the headquarter, and calculated annual returns.6 Adjusted prices and number of shares outstanding

enable us to compute market valuation.

We use returns and market valuation of financial institutions to compute value-weighted market

return indices of the financial sector of each country. Because we need yearly financial institution

level variables to assess the impact of fintech formation on financial institutions performance, we

collapsed all daily firm data to yearly data. To test hypothesis 1 and in line with Phan et al. (2020),

we calculate the net interest margin, return on assets, return on equity, and Tobin’s Q as measures

of financial institutions performance. Tobin’s Q traditionally measures the sum of the market value

of equity and the book value of liabilities divided by the book value of total assets. We compute

financial institutions’ performance also based on a market measure. The choice to study annual

stock returns is motivated by the fact that stock prices better reflect current information and

expectations about firms’ future profitability and growth (Anilowski et al., 2007).

To test hypothesis 2a, we use accounting and market measures of risk in our analysis. The first

measure of financial institution default risk is the Z-score of each financial institution, which equals

the return on assets plus the capital-asset ratio divided by the standard deviation of the return on

assets. The Z-score thus measures the number of standard deviations below the mean by which

5
 Because of data limitations in our explanatory variables and given that we use a lag of one year, our sample reduces
to the period from 2006 to 2018 covering only 6,406 financial institutions.
6
 For the CompuStat World Database, we compute returns by considering adjustment factors according to the
guidelines from the CompuStat manuals.

 16

profits would have to fall to deplete the financial institution’s equity capital completely (Boyd et

al., 2006). The measure has a long tradition in the finance literature (Roy, 1952) and is still used in

empirical research to capture a financial institution’s distance from default (Bhagat et al., 2015;

Pathan, 2009; Houston et al., 2010, Jin et al., 2013; Laeven and Levine, 2009). A higher value of

the Z-score indicates a lower default risk and greater stability of the respective financial institution.

Because the Z-score is often highly skewed, we follow Laeven and Levine (2009) and use the

natural logarithm of the Z-score in our estimations. Our second measure of financial institution

default risk is the volatility of stock returns, which has been widely used in prior research (Brown

et al., 2015; Pathan, 2009; Sun and Liu, 2014). It captures the market’s perception of the risk

inherent in banks’ assets, liabilities, and off-balance sheet positions (Pathan, 2009).

To test hypothesis 2b, we consider the marginal expected shortfall, which captures a financial

institutions’ exposure to systemic risk. It measures the average of individual stock returns on a

subset of sample days that corresponds with the 5 percent worst days of the equally weighted

market index.

3.2 Explanatory variables

The data source for our explanatory variable of interest is the CrunchBase database, which contains

detailed information on fintech startup formations and their financing. The database is assembled

by more than 200,000 company contributors, 2,000 venture partners, and millions of web data

points7 and has recently been used in scholarly articles (Bernstein et al., 2017; Cumming et al.,

2016, Haddad and Hornuf, 2019). We retrieved the data that we use in our analysis on July 9, 2019.

Because CrunchBase might collect some of the information with a time lag, the observation period

7
 See https://about.crunchbase.com.

 17

in our sample ends on December 31, 2018. Overall, we identified 12,549 fintech startups from 87

countries for our relevant sample period.

To account for financial institution and cross-country heterogeneity, we consider several variables,

which are frequently used as controls in the bank performance literature (Agoraki et al., 2011;

Tabak et al., 2012, Phan et al., 2020). Following Pathan and Faff (2013), Shaban and James (2018),

Dietrich and Wanzenried (2014) and Berger et al., 2017,we control for total assets as a measure for

average firm size, the capital ratio, the cost income ratio, the interest income margin, and the book

to market ratio. All variables were retrieved from the CRSP and CompuStat databases. To address

country-time specific heterogeneity, we consider several macroeconomic indicators. We control

for the cross domestic product (GDP), because it might influence bank performance through the

business cycle. When the economy faces a recession or an economic crisis, the quality of borrowers

deteriorates, which in turn worsens banks’ loan portfolio and affects bank performance. On the

loan demand side, borrowers are less willing to invest in long term projects in times of crisis and

prefer to cut their spending. Not surprisingly, the empirical literature shows that economic growth

also stimulates the financial system (Athanasoglou et al., 2008; Albertazzi and Gambacorta, 2009).

We also account for inflation as a measure of financial institutions performance, because earlier

research shows a positive relationship between inflation and profits (Kasman et al. 2010; Trujillo-

Ponce, 2013). However, if inflation is anticipated and financial institutions fail to adjust their

interest rate, costs can increase faster than profits, which negatively affects bank performance.

Therefore, the effect of inflation on bank performance is ambiguous.

To control for the extent to which countries’ political decisions affects bank performance, we

include the variable size of government, which combines five components: Government

consumption, transfers and subsidies, government entreprises and investment, top marginal tax rate

 18

and state ownership of assets. The variable ranges from 0 to 10, with higher values indicating that

countries rely more on personal choice and markets rather than government budgets and political

decision-making. To control for differences in the efficiency of legal protection and enforcement

of laws across economies, we consider the variable legal protection that was curated by the Fraser

Institute database. It entails several legal system components, including rule of law, security of

property rights, an independent and unbiased judiciary, and impartial and effective enforcement of

the law. These components are indicators of how effectively the protective functions of the legal

system are performed. The variable ranges from 0 to 10, with higher values indicating better

government efficiency in terms of legal protection.

Finally, we control for the impact the concentration of banks has on bank performance. Empirical

research still shows ambiguous results for this variable. In the European context, Delis and Tsionas

(2009) find that firms with market power tend to operate inefficiently, because managers enjoy

monopoly profits. Another study by Maudos and Fernandez de Guevara (2007) finds no evidence

for a significant relation between firm concentration and performance. The measure was extracted

from the World Bank database. The variable is defined as the sum of the market share in terms of

total assets of the three largest banks. Definitions of all variables and their sources are reported in

the Appendix Table A2.

3.3 Model specifications

Our empirical approach is motivated by recent research estimating determinants of bank

performance (Köster and Pelster, 2017; Shaban and James, 2018, Phan et al., 2020). To test our

hypotheses, we use a two-step generalized method of moments (GMM) system dynamic panel

estimator (Arellano and Bover, 1995; Blundell and Bond, 1998). This approach allows us to treat

 19

the explanatory variables as endogenous respectively using their past values as instruments

(Wintoki et al., 2012). First differences help to eliminate time-invariant unobserved heterogeneity

and thus omitted variable bias. Regarding the lagged explanatory variables of the dependent

variable, it is important to determine the correct number of lags to sufficiently capture the past. We

argue that older lags are more likely to be exogenous with respect to the residuals of the present

and should therefore be valid instruments. We follow Wintoki et al. (2012) and include two lags to

capture the persistence of performance of financial institutions. Our baseline regression model is:

 , = + 1 , −1 + 2 , −1 + 3 , −2 + 4 , + 5 , + 6 , + 7 , + 8 , + 9 , +

 10 , + 11 , + 12 , + 13 , + , 

whereby PER represents one of five different dependent variables: net interest margin, return on

assets, return on equity, Tobin’s Q, and annual stock return. Analogously, we estimate a two-step

GMM system dynamic panel model to test hypothesis 2a and 2b:

 , = + 1 , −1 + 2 , −1 + 3 , −2 + 4 , + 5 , + 6 , + 7 , + 8 , + 9 , 

+ 10 , + 11 , + 12 , + 13 , + , 

whereby RISK represents one of three different dependent variables: z-Score, stock return volatility,

and marginal expected shortfall. We use year dummies in all models to account for business cycle

effects. In addition, we use heteroscedasticity-robust standard errors clustered at the financial

institution level.

 20

4. Results

4.1. Benchmark model

Table 1 reports the baseline regression. Columns represent the five dependent variables measuring

performance: net interest margin, return on assets, return on equity, Tobin’s Q, and annual stock

return. We find sector specific and macro-level variables to have an economically meaningful and

statistically significant impact on financial institutions’ performance. The control variables that are

significant performance predictors in three models are lagged cost income ratio and market to book

ratio. Inflation and bank concentration are statistically significant in two of five models, the

interest income margin is significant in one of the models.

 --- Table 1 About Here ---

4.2. Lag effect of fintech startup formations on bank performance and risk taking

In Table 2, we examine whether fintech formations positively affect the performance of financial

institutions. In four out of five models, the coefficient of fintech is statistically different from zero.

The number of fintech startup formations in a country positively predicts net interest margin, return

on assets, the return on equity, and annual stock returns of traditional financial institutions. The

coefficients imply that 10 extra fintech firms entering the market in a given year increase financial

institutions net interest margin by 1.7%, return on assets by 34.6%, return on equity by 8.1% and

annual stock returns by 78.9% of the mean value, respectively. For Indonesia, Phan et al. (2020)

find net interest margin to change by 5.3%, return on assets by 93.2%, and return on equity 27.3%

for 10 extra fintech startups entering the market.

 --- Table 2 About Here ---

 21

Next, we test whether the effect of fintech startup formations on financial institutions’ performance

differs for large and small institutions. Recent research suggests that financial characteristics of

institutions are important predictors of their performance (Dietrich and Wanzenried, 2011; Köster

and Pelster, 2017; Talavera, Yin and Zhang, 2018). We consider the market value of a financial

institutions as proxy to differentiate large, universal financial institutions from small, specialized

financial institution. We expect large, universal financial institution to adapt their business models

slower than smaller, specialized financial institution, which presumably have already specialized

in business models such as BaaP and BaaS. On the other hand, large financial institutions often

have deeper pockets and can more forcefully pursue change through acquisitions and inhouse

experimentation. Our results show a positive and significant association between the formation of

fintech startups and large financial institutions performance. We find that for financial institutions

with above median market value, fintech startup formations have a positive and robust effect on

three of five measures for financial institutions’ performance—return on assets, return on equity,

and annual stock return. For financial institutions with below median market value, we do not

observe any significant association between fintech formations and financial institutions

performance. Large, universal institutions might benefit from alliances with fintechs, for example,

through product related corporations or partial acquisitions of fintechs, which help them to obtain

specialized knowledge and improve their performance (Hornuf et al. 2020). This result does not

necessary imply that smaller financial institutions are reluctant to change. In fact, smaller, more

specialized financial institutions might already possess a more modern IT infrastructure and hence

benefit only at the margin from fintech startups.

 --- Table 3 About Here ---

 22

Recent research posits a non-linear relationship between fintech formations and the behaviors of

financial institutions over time (Wang, Liu, and Luo; 2020). The relationship is explained by the

initial threat that fintech startups posed to traditional financial institutions, especially during and

shortly after the 2007-2008 financial crisis, which later turned towards more cooperative business

relations. We suspect fintechs to have exercised more pressure on incumbent institutions during

the first wave of fintech startups formations, while later this pressure has relaxed as traditional

financial institutions acquired fintech and adapted their business models. Acquisitions and alliances

may, however, not unfold their full value, if incumbent institutions simply eliminate an unpopular

competitor from the market. A recent event study shows that at least in the short run announcement

of bank–fintech alliances are perceived negative by the market (Hornuf et al. 2020). In a next step,

we therefore divide our sample into two subsamples and test whether the development of fintech

startups has a differential impact on financial institutions’ performance for the periods from 2005

to 2011 and from 2012 to 2018.

The results in Table 4 show that fintech positively affect bank performance during the period from

2005 to 2011 for return on assets and return on equity. During the later period from 2012 to 2018

the impact of fintech startup formations on financial institutions’ performance is only positive and

significant at conventional levels for Tobin’s Q. For net interest margin, return on assets, and return

on equity we still find a positive, but only weakly significant association between fintech startup

formations and financial institutions performance. Thus, it appears that the pressure resulting from

fintech startups following the financial crisis vanished over time potentially as a result of more

cooperative business models, although the positive association between performance and fintech

formations has not entirely disappeared in recent years.

 --- Table 4 About Here ---

 23

In Table 5, we test whether fintech startup formations predict the default risk of financial

institutions. The columns report estimates for our dependent variables of interest—z-Score, stock

return volatility, and marginal expected shortfall.

Fintech startup formations have a negative and statistically weakly significant effect on the

accounting measure Z-score. If this result stems from a lack of statistical power, it would indicate

that fintech startups are associated with a higher probability of default of financial institutions.

However, the result we have obtained for the Z-score are not only weakly significant, they should

also be interpreted with cautious. First, the Z-score computation is based on accounting data, which

is only as good as the underlying accounting and auditing framework. The case of Wirecard

constitutes a recent example that accounting measures might not reflect the actual situation of a

financial institution. Moreover, if financial institutions are able to smooth out the reported data, the

Z-score provides an overly positive assessment of the financial institution’s stability.

Using a market measure for our dependent variable, we find that the development of the fintech

sector has decreased financial institutions’ stock return volatility. Finally, the stock return volatility

looks at each financial institution separately, neglecting that a default of one financial institution

may cause losses to other financial institution in the system. During the 2007-2008 financial crises,

it became evident that many financial institutions are interconnected and market contagion occurs

as a domino effect. Using the marginal expected shortfall as our dependent variable, we capture

the effect that fintech formations have on the financial institutions’ exposure to systemic risk. We

find that the development of fintech startups decreases incumbents’ exposure to systemic risk. The

spread of fintechs might not only result in more competition and better performance of traditional

financial institutions, it increasingly diversifies the use and execution of financial services over

 24

different market players. In this sense, the rise of fintech might to some degree counteract the too-

systemic-to-fail problematic.

 --- Table 5 About Here ---

To test the robustness of our results, we calculate the ratio of total fintech founded per year and

country and divide them by the total number of startups founded during that year in the respective

economy as an alternative measure of fintech startup formations. The data for startup formations

was obtained from the Crunchbase database. Table 6 reports that the results are very similar to

previous findings and evidences that fintech formations positively predict bank performance. With

regard to default risk, we also find that fintech startup formations negatively affect financial

institutions’ default risk, which is illustrated by the decrease in stock return volatility.

 --- Table 6 About Here ---

5. Discussion and Conclusion

The article investigates whether fintech startup formations affect financial institution’ performance

and default risk. We evidence that fintech startup formations improve financial institution’

performance in terms of accounting and market measures. These findings are in line with previous

research (Vives, 2019), which posits that banks rethink and reshape their business model when

confronted with competitive pressure. One potential way how financial institutions accomplish

performance improvements when confronted with fintechs is through cooperation with and

integration of the new players in their organization (Hornuf et al. 2020). Moreover, we use the

 25

marginal expected shortfall as a measure of systemic risk and find that financial institutions

exposure to systemic risk decrease with more fintech startups entering the market.

The latter finding sheds light on how financial institutions benefit from technology spillovers when

confronted with novel technological solutions that were developed by fintechs (Blalock and

Gertler, 2008; Newman et al., 2015). Technological improvements and new business models

improve the efficiency of risk management and consequently reduce default risk. To provide an

example, the Industrial and Commercial Bank of China intercepted approximately 900,000 risky

transactions by employing digital technology in 2018, which significantly reduced ICBC’s credit

risk (Cheng and Qu, 2020). Moreover, the blockchain technology and cloud computing cater

decentralized, real-time transactions, which could improve financial institutions risk management

and may reduce their contribution to systemic risk.

Our analysis has some clear limitations. While we find evidence that fintech startups have a positive

effect on financial institutions’ performance, the same might not hold for large technology

companies such as Alibaba, Alphabet (Google), Amazon, Apple, Facebook, Microsoft, and

Tencent, which have started to implement and offer financial services to their customers as well.

In fact, these companies are not only interconnected with large parts of the real economy, they are

also by themself systemically relevant. For example, Amazon operates their own payment service

(Amazon Pay), lending business (Amazon Lending), and cloud computing business (Amazon Web

Services). Although these services are all operated by formally independent companies, no one can

so far foresee how a default of one of these companies will affect the other services provided by

Amazon. Thus, fintech services provided by large technology companies might negatively impact

financial institutions performance, not least due to their sheer size and market power, and could

also negatively affect systemic risk.

 26

When comparing the period from 2005 to 2011 and from 2012 to 2018, we find that the pressure

resulting from fintech startups on financial institutions performance has somewhat vanished,

although the positive association has not entirely disappeared yet. Future research might thus

investigate whether the relationship has completely disappeared by now and which impact large

technology companies have on financial institutions performance and default risk. Moreover, while

we have investigated the overall effect of fintech startup formations on the performance and default

risk of incumbent financial institutions, information systems and finance scholars might

disentangle in more detail through which channel fintechs impact the performance and default risk

of incumbents. Such research most likely has to be based on case studies and/or experimental

interventions on individual branches of financial institutions.

 27

Reference
Aaker, D. A., & Keller, K. L. (1990). Consumer evaluations of brand extensions. Journal of
 Marketing, 54(1), 27-41.
Acar, O., & Çıtak, Y. E. (2019). Fintech integration process suggestion for banks. Procedia
 Computer Science, 158, 971-978.
Acharya, V., & Naqvi, H. (2012). The seeds of a crisis: A theory of bank liquidity and risk taking
 over the business cycle. Journal of Financial Economics, 106(2), 349-366.
Adesina, K. S. (2021). How diversification affects bank performance: The role of human
 capital. Economic Modelling, 94, 303-319.
Aebi, V., Sabato, G., & Schmid, M. (2012). Risk management, corporate governance, and bank
 performance in the financial crisis. Journal of Banking & Finance, 36(12), 3213-3226.
Afonso, G., Santos, J. A., & Traina, J. (2015). Do 'Too-Big-To-Fail' Banks Take on More
 Risk?. Journal of Financial Perspectives, 3(2).
Aghion, P., & Bolton, P. (1992). An incomplete contracts approach to financial contracting. The
 Review of Economic Studies, 59(3), 473–494
Agoraki, M. E. K., Delis, M. D., & Pasiouras, F. (2011). Regulations, competition and bank risk-
 taking in transition countries. Journal of Financial Stability, 7(1), 38-48.
Agoraki, M. E. K., Delis, M. D., & Staikouras, P. K. (2010). The effect of board size and
 composition on bank efficiency. International Journal of Banking, Accounting and
 Finance, 2(4), 357-386.
Albertazzi, U., & Gambacorta, L. (2009). Bank profitability and the business cycle. Journal of
 Financial Stability, 5(4), 393-409.
An, J., & Rau, R. (2019). Finance, technology and disruption. The European Journal of Finance,
 1-12.
Angkinand, A., & Wihlborg, C. (2010). Deposit insurance coverage, ownership, and banks' risk-
 taking in emerging markets. Journal of International Money and Finance, 29(2), 252-274.
Anilowski, C., Feng, M., & Skinner, D. J. (2007). Does earnings guidance affect market returns?
 The nature and information content of aggregate earnings guidance. Journal of Accounting and
 Economics, 44(1-2), 36-63.
Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-
 components models. Journal of Econometrics, 68(1), 29-51.
Ashraf, B. N. (2017). Political institutions and bank risk-taking behavior. Journal of Financial
 Stability, 29, 13-35.
Athanasoglou, P. P., Brissimis, S. N., & Delis, M. D. (2008). Bank-specific, industry-specific and
 macroeconomic determinants of bank profitability. Journal of International Financial Markets,
 Institutions and Money, 18(2), 121-136.
Barth, J. R., Caprio Jr, G., & Levine, R. (2004). Bank regulation and supervision: What works
 best? Journal of Financial Intermediation, 13(2), 205-248.
Beck, T., De Jonghe, O., & Schepens, G. (2013). Bank competition and stability: Cross-country
 heterogeneity. Journal of Financial Intermediation, 22(2), 218-244.

 28

Beck, T., Demirgüç-Kunt, A., & Levine, R. (2006). Bank concentration, competition, and crises:
 First results. Journal of Banking & Finance, 30(5), 1581-1603.
Berger, A. N., Black, L. K., Bouwman, C. H., & Dlugosz, J. (2017). Bank loan supply responses
 to Federal Reserve emergency liquidity facilities. Journal of Financial Intermediation, 32, 1-
 15.
Berger, A. N., Hasan, I., & Zhou, M. (2010). The effects of focus versus diversification on bank
 performance: Evidence from Chinese banks. Journal of Banking & Finance, 34(7), 1417-1435.
Bernstein, S., Korteweg, A., & Laws, K. (2017). Attracting early stage investors: Evidence from a
 randomized field experiment. The Journal of Finance, 72(2), 509-538.
Bertsch, C., Hull, I., Qi, Y., & Zhang, X. (2020). Bank misconduct and online lending. Journal of
 Banking & Finance, 116, 105822.
Bhagat, S., Bolton, B., & Lu, J. (2015). Size, leverage, and risk-taking of financial
 institutions. Journal of Banking & Finance, 59, 520-537.
Bhalla, R. (2019). FinTech innovation: Revolutionary or evolutionary business model
 disruption?. Journal of Digital Banking, 4(2), 102-110.
Bitar, M., & Tarazi, A. (2019). Creditor rights and bank capital decisions: Conventional vs. Islamic
 banking. Journal of Corporate Finance, 55, 69-104.
Blalock, G., & Gertler, P. J. (2008). Welfare gains from foreign direct investment through
 technology transfer to local suppliers. Journal of International Economics, 74(2), 402-421.
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data
 models. Journal of Econometrics, 87(1), 115-143.
Board, F. S. (2017). Fintech Credit, market structure, business models and financial stability
 implications. Committee on Global Financial System.
Borio, C., & Zhu, H. (2012). Capital regulation, risk-taking and monetary policy: a missing link in
 the transmission mechanism?. Journal of Financial stability, 8(4), 236-251.
Boyd, J. H., & De Nicolo, G. (2005). The theory of bank risk taking and competition revisited. The
 Journal of Finance, 60(3), 1329-1343.
Boyd, J. H., De Nicolò, G., & Jalal, A. M. (2006). Bank risk-taking and competition revisited:
 New theory and new evidence. Available at SSRN:
 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=956761
Brahmana, R., Kontesa, M., & Gilbert, R. E. (2018). Income diversification and bank performance:
 evidence from Malaysian banks. Economics Bulletin, 38(2), 799-809.
Brandl, B., & Hornuf, L. (2020). Where did FinTechs come from, and where do they go? The
 transformation of the financial industry in Germany after digitalization. Frontiers in Artificial
 Intelligence, 3, 8.
Brown, K., Jha, R., & Pacharn, P. (2015). Ex ante CEO severance pay and risk-taking in the
 financial services sector. Journal of Banking & Finance, 59, 111-126.
Bruno, G. S., Marelli, E., & Signorelli, M. (2014). The rise of NEET and youth unemployment in
 EU regions after the crisis. Comparative Economic Studies, 56(4), 592-615.
Buchak, G., Matvos, G., Piskorski, T., & Seru, A. (2018). Fintech, regulatory arbitrage, and the
 rise of shadow banks. Journal of Financial Economics, 130(3), 453-483.

 29