1
Lars HornufUniversität BremenMax Planck Institut für Innovation und WettbewerbCESifo
How Do Banks Interact with Fintechs?Forms of Alliances and Their Impact on Bank Value
with Milan KlusTodor Lohwasser
Armin Schwienbacher
Motivation
2
§ Financial industry spent large sums on information technology(Scott et al., 2017)
§ Banks perceived as least innovative§ Fintechs reshape and crowd out business models of banks§ Banks engage in strategic alliances with newcomers
This Paper
3
§ We investigate …§ the extent to which banks interact with fintechs§ different forms of interactions§ what drives banks to engage in strategic alliances – including
minority or majority investments – with fintechs§ the impact of strategic alliances on bank value
Preview of Findings
4
Implementation of a digitalization strategy or employment of a Chief Digital Officer by banks has a positive effect on alliances with fintechs1
2
3
Large banks more willing to engage financially in fintech firms; banks more likely to invest in small rather than large fintechs
Announcements of alliances have negative effect on bank value; digital banks benefit from alliances
5
Literature and Hypotheses
Literature
6
§ Academic literature on fintech still scarce§ Puschmann (2017) categorizes the industry§ Haddad & Hornuf (2018) study determinants of fintech formations§ Cumming & Schwienbacher (2018) study fintech venture capital§ Navaretti et al. (2017) run a conceptual analysis on bank-fintech alliances
§ Focus on developments in specific fintech sectors§ D’Acunto et al. (2018) study performance of robo-advisor§ Hornuf et al. (2018) study performance of equity crowdfunding§ Yermack (2017) studies corporate governance and the blockchain§ …
Hypotheses
7
§ Alliances mutually beneficial transactions between banks and fintechs
§ H1: CDO and digitalization strategy ceteris paribus increase banks’ propensity to engage in an alliance with a fintech
§ H2A: Large banks more likely to acquire fintech startups; small banks engage in product-related collaborations§ Large banks have deeper pockets and take the risk of an acquisition§ Banks specialize in IT services to corporate clients, e.g. solarisBank à API
banking, BaaP and BaaS
Hypotheses
8
§ H2B: Banks more likely to invest in small rather than large fintechs§ Innovations of early-stage firms difficult to contract, because ultimate
outcome hard to determine ex ante and non-verifiable ex post (Aghion and Bolton, 1992)
§ Larger fintechs have their own distribution channel and end-user base àless synergies through acquisition
9
Data and Variables
Data
10
§ Hundred largest banks respectively in Canada, France, Germany, and the United Kingdom from 2007 to 2017
§ Hand-collected information on strategic alliances and CDOs from annual reports
§ Hand-collected information on 500 bank-fintech alliances from bank websites, Crunchbase and news articles on Factiva
§ Control variables such as bank is listed, universal bank, bank total assetsand ROAA from Fitch Connect database and annual reports
Measurement of Variables
11
30
Table 1 This table provides the definition of variables included in the regression models. Here, (d) indicates a dummy variable.
Variable Name Definition Dependent variables
Alliance (d) Binary variable equal to 1 if the bank of interest engaged in at least one
alliance with a FinTech and 0 otherwise. We define alliance as any type of contract- or investment-based interaction between a bank and fintech with the purpose to collaborate. From the year the bank first announced an alliance, we presume that the bank held on to this strategy and coded the variable accordingly. Source: Bank websites, Factiva, Google, Thomson Reuters M&A Database.
Number of New Alliances Number of fintech alliances for a given bank and year. Investment (d) Binary variable equal to 1 if a bank acquired at least a minority stake of a
fintech and equal to 0 if the alliance is characterized by a product-related collaboration. Other forms of alliances are excluded for this variable. Source: Bank websites, crunchbase, Factiva, fintech websites, Google, Thomson Reuters M&A Database.
CAR(-X;+Y) The cumulative abnormal return for the event window (-X;+Y). Event date 0 constitutes the date of the first public announcement of the alliance. In the analysis, we specify different windows. Source: Thomson Reuters Datastream and own calculations.
Bank characteristics Bank is Listed (d) Binary variable equal to 1 if the bank is publicly listed in a given year and
zero otherwise. Source: Onvista. Bank Headquarter (d) Binary variable equal to 1 if the bank is located in the same country as the
headquarter of the bank and 0 otherwise. Source: Crunchbase, Fitch connect, fintech websites.
Bank Loan-To-Assets Ratio Ratio of a bank’s total loans outstanding over its total assets. Source: Fitch connect.
Bank ROAA Ratio of a bank’s return over its average assets. Source: Fitch connect.
Chief Digital Officer (d) Binary variable equal to 1 if the bank employs a Chief Digital Officer (CDO) in the year of interest and 0 otherwise. Source: Annual reports, LinkedIn, Bank website.
Digital Bank (d) Binary variable equal to 1 if the bank is a direct bank without a branch network, offering only remote services via online- and telephone banking, and 0 otherwise. Source: Bank websites.
Digital Strategy (d) Binary variable equal to 1 if the bank has announced a clear digital strategy, which entails a roadmap of concrete measures and actions in their annual report. From the year the bank first announced a digital strategy, we presume that the bank held on to this strategy and coded the variable accordingly. Source: Annual reports.
ln(Bank Age) Natural logarithm of the bank’s age in years. Source: Fitch connect. ln(Bank Total Assets) Natural logarithm of the bank’s total assets. Source: Fitch connect. Universal Bank (d) Binary variable equal to 1 if the bank participates in various banking
services and 0 otherwise. Source: Bank websites. Fintech characteristics
Fintech Employees (rank) Range of the fintech’s employees. Categories: 1-10, 11-50, 51-100, 101-1000, >1000. Source: Crunchbase, fintech website, LinkedIn.
Measurement of Variables
12
13
Results
Summary Statistics of Panel Data
14
32
Table 2.1. Summary statistics of panel data for the full sample of 4400 bank-year observations by the 100 largest banks each in Canada, France, Germany, and UK from 2007 to 2017 included in our sample. The table provides the mean, different standard deviations, and the number of banks and observations of the panel. Here, (d) indicates a dummy variable.
Variable Mean SD (Overall)
SD (Between)
SD (Within)
Number of Banks
Number of Observations
Dependent variables Alliances (d) 0.12 0.32 0.20 0.25 400 4400 Nbr. New Alliances 0.11 0.72 0.35 0.63 400 4400 Explanatory variables
Digital Strategy (d) 0.21 0.41 0.25 0.34 327 3394 Chief Digital Officer (d) 0.03 0.16 0.10 0.13 353 3871 Bank is Listed (d) 0.15 0.35 0.35 0.00 400 4400 Digital Bank (d) 0.07 0.26 0.26 0.00 400 4400 Universal Bank (d) 0.40 0.49 0.49 0.00 400 4400 Bank HQ Country of Interest 0.82 0.38 0.38 0.00 398 4378 Ln(Bank Age) 3.83 0.96 0.96 0.00 371 4081 Ln(Bank Total Assets) 16.65 2.41 2.26 1.06 375 3345 Bank Loan-to-Assets Ratio 0.57 0.26 0.26 0.08 366 3211 Bank ROAA 0.01 0.04 0.04 0.03 374 3191
Fintech Segments
15
29
Fig. 1. Fintech segments by bank country. This figure presents the frequency of occurrence of bank alliances with fintechs by segment and country. The sample includes 492 fintechs from 27 countries collected from 2007 to 2017. The bars represent the number of fintechs in each segment and grouped by the bank’s home country.
Fig. 2. Forms of interaction by country. This figure presents the frequency of occurrence of interacting fintechs by form and country. The sample includes 469 interacting fintechs from 28 countries collected from 2007 to 2017. The bars represent the frequency of the different arrangements of interaction with banks in Canada, France, Germany, and UK.
(1) Financing(3) Payment services(5) Asset management & payment services(7) Other segment
(2) Asset management(4) Bank-level software(6) Cyber security
(1) Majority investment(3) Product-related collaboration
(2) Minority investment(4) Other form of alliance
Results for Alliance Dummy
16
34
Table 3 Panel data analysis for the dummy variables Digital Strategy, Chief Digital Officer, and Alliance. This table presents the results of random-effects probit regressions modeling the probability that at least one interaction between bank i and a fintech occurs in year t (dependent variable = 1) or not (dependent variable = 0), based on the full sample. The coefficients show the average marginal effects with standard errors in parentheses. All the variables are defined in Table 1. * denotes significance at the 5% level, ** at the 1% level, and *** at the 0.1 % level. Here, (d) indicates a dummy variable.
(1) (2) (3) (4) full sample
Dependent Variable: Alliance (d) Explanatory variables
Digital Strategy (d) 0.060*** 0.077*** (0.009) (0.016)
Chief Digital Officer (d) 0.095*** 0.072 (0.022) (0.039)
Bank is Listed (d) 0.062*** 0.099*** 0.054* 0.090*** (0.016) (0.018) (0.021) (0.018)
Digital Bank (d) 0.003 0.044* 0.078** 0.077** (0.021) (0.018) (0.027) (0.026)
Universal Bank (d) 0.028** 0.036*** 0.040* 0.035* (0.010) (0.010) (0.018) (0.015)
Bank Headquarter (d) 0.001 0.011 0.006 0.025 (0.015) (0.014) (0.030) (0.025)
ln(Bank Age) 0.011* 0.013* 0.018* 0.027** (0.005) (0.005) (0.009) (0.009)
ln(Bank Total Assets) 0.013*** 0.009*** (0.004) (0.003)
Bank Loan-to-Assets Ratio -0.033 0.003 (0.044) (0.031)
Bank ROAA -0.145 -0.318 (0.135) (0.409) Year Dummies Yes Yes Yes Yes Country Dummies Yes Yes Yes Yes n (Obs.) 3344 3816 2421 2743 N (Banks) 322 348 297 318 Wald chi2 432.40 323.41 313.86 326.96 Prob > chi2 0.000 0.000 0.000 0.000
35
Table 4 Panel data analysis for the variables Digital Strategy, Chief Digital Officer, and Number of Partnerships. This table presents the results of random-effects negative binominal regressions. The dependent variable represents the number of new alliances of bank i in year t. We report incident-rate ratios with standard errors in parentheses. Models 1–4 use the full sample; Models 5–8 exclude fintechs with more than 1000 employees or fintechs which were more than 10 years old at the time of the alliance. All the variables are defined in Table 1. A Hausman test is used to identify whether fixed effects or random effects should be applied to each respective model. * denotes significance at the 5% level, ** at the 1% level, and *** at the 0.1% level. Here, (d) indicates a dummy variable. (1) (2) (3) (4) (5) (6) (7) (8)
full sample excluding large fintechs Dependent Variable: Number of Alliances Explanatory variables
Digital Strategy (d) 4.097*** 3.133*** 4.791*** 3.555*** (0.880) (0.669) (1.238) (0.915)
Chief Digital Officer (d) 2.721*** 1.945** 2.815*** 1.810* (0.614) (0.440) (0.736) (0.466)
Bank is Listed (d) 2.679*** 4.061*** 1.913** 2.703*** 3.529*** 5.160*** 1.953** 2.715*** (0.615) (0.927) (0.397) (0.579) (0.927) (1.377) (0.468) (0.666)
Digital Bank (d) 1.418 1.946* 2.174** 2.225** 1.597 2.477** 2.019* 2.295** (0.443) (0.597) (0.585) (0.621) (0.558) (0.850) (0.634) (0.727)
Universal Bank (d) 1.715** 1.719** 1.534* 1.506* 1.663* 1.682* 1.445 1.397 (0.341) (0.338) (0.283) (0.290) (0.383) (0.382) (0.313) (0.313)
Bank Headquarter (d) 0.876 1.003 0.814 0.944 0.739 0.967 0.761 0.996 (0.242) (0.273) (0.207) (0.253) (0.237) (0.308) (0.224) (0.311)
ln(Bank Age) 1.112 1.171 1.144 1.213* 1.183 1.253* 1.121 1.213 (0.106) (0.108) (0.100) (0.108) (0.130) (0.133) (0.113) (0.121)
ln(Bank Total Assets) 1.283*** 1.248*** 1.357*** 1.338*** (0.060) (0.057) (0.074) (0.070)
Bank Loan-to-Assets Ratio 0.706 0.961 0.871 1.105 (0.258) (0.352) (0.386) (0.483)
Bank ROAA*102 0.876** 0.876* 0.859* 0.899 (0.040) (0.054) (0.054) (0.070) Year Dummies Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies Yes Yes Yes Yes Yes Yes Yes Yes n (Obs.) 3344 3816 2421 2743 3344 3816 2421 2743 N (Banks) 322 348 297 318 322 348 297 318 Wald chi2 369.45 400.14 401.58 415.12 282.23 319.36 313.91 327.84 Prob > chi2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Results for # FinTech Alliances
17
Summary Statistics of Deal Level Data
18
33
Table 2.2. Summary statistics of deal-level data for the full sample of 500 alliances identified between banks and fintechs in Canada, France, Germany and UK from 2007 to 2017. The table provides the number of observations, mean, median, standard deviation, minimum and maximum of the deal-level data. Here, (d) indicates a dummy variable.
Variable Nbr. Obs. Mean Median Std. Dev. Minimum Maximum Dependent variables Financial Investment (d) 455 0.44 0 0.50 0 1 Explanatory variables Digital Strategy (d) 470 0.86 1 0.35 0 1 Chief Digital Officer (d) 489 0.23 0 0.42 0 1 Fintech Employees (rank) 462 2.40 2 1.19 1 5 Ln(Bank Total Assets) 362 18.99 19.79 2.26 12.53 22.73 Bank is Listed (d) 500 0.56 1 0.50 0 1 Digital Bank (d) 500 0.11 0 0.31 0 1 Universal Bank (d) 500 0.67 1 0.47 0 1 Bank HQ Country of Interest 500 0.87 1 0.34 0 1 Ln(Bank Age) 498 4.08 4.14 0.93 1.10 5.86 Bank ROAA 460 0.00 0 0.01 -0.07 0.04 Fintech Front End Solution 463 0.71 1 0.46 0 1 Fintech HQ Country of Interest (d) 493 0.65 1 0.48 0 1 Fintech Nbr. Patents 500 1.67 0 8.49 0 158 Fintech Age 456 5.67 4 6.41 0 45
Forms of Alliances
19
29
Fig. 1. Fintech segments by bank country. This figure presents the frequency of occurrence of bank alliances with fintechs by segment and country. The sample includes 492 fintechs from 27 countries collected from 2007 to 2017. The bars represent the number of fintechs in each segment and grouped by the bank’s home country.
Fig. 2. Forms of interaction by country. This figure presents the frequency of occurrence of interacting fintechs by form and country. The sample includes 469 interacting fintechs from 28 countries collected from 2007 to 2017. The bars represent the frequency of the different arrangements of interaction with banks in Canada, France, Germany, and UK.
(1) Financing(3) Payment services(5) Asset management & payment services(7) Other segment
(2) Asset management(4) Bank-level software(6) Cyber security
(1) Majority investment(3) Product-related collaboration
(2) Minority investment(4) Other form of alliance
Investment vs. Product Related Collaboration
20
36
Table 5 Cross-sectional regression results for Financial Investment versus Product-related Collaboration. This table presents the results of a Probit regression. The coefficients show the average marginal effects, and standard errors are clustered by banks (in parentheses). The dependent variable Financial Investment is equal to 1 if the bank invests in a fintech and to 0 if the alliance is characterized by a product-related collaboration. Models 1 and 2 use the full sample; Models 3 and 4 fintechs with more than 1000 employees or fintechs which were more than 10 years old at the time of the alliance. All the variables are defined in Table 1. * denotes significance at the 5% level, ** at the 1% level, and *** at the 0.1% level. Here, (d) indicates a dummy variable.
(1) (2) (3) (4) full sample excluding large fintechs Dep. Var.: Financial Investment (d) Explanatory variables Digital Strategy (d) -0.069 -0.078 (0.083) (0.104) Chief Digital Officer (d) -0.082 -0.113 (0.118) (0.122) Fintech Employees (rank) -0.098*** -0.095*** -0.037 -0.034 (0.026) (0.026) (0.035) (0.036) ln(Bank Total Assets) 0.057** 0.050** 0.048* 0.041 (0.019) (0.018) (0.022) (0.021) Bank is Listed (d) 0.107 0.132 0.16 0.186 (0.114) (0.126) (0.123) (0.134) Digital Bank (d) 0.181 0.185 0.205 0.204 (0.121) (0.119) (0.121) (0.119) Universal Bank (d) -0.157* -0.137* -0.204* -0.176* (0.070) (0.067) (0.080) (0.080) Bank HQ Country of Interest (d) -0.051 -0.080 -0.003 -0.055 (0.076) (0.104) (0.073) (0.105) ln(Bank Age) 0.010 0.004 0.022 0.014 (0.044) (0.041) (0.046) (0.043) Bank ROAA 4501 2589 2.515 0.654 (7.238) (7.091) (6.930) (7.344) Fintech Front End Solution (d) -0.032 -0.039 -0.034 -0.043 (0.054) (0.055) (0.060) (0.061) Fintech HQ Country of Interest (d) -0.143** -0.138** -0.094 -0.084 (0.055) (0.053) (0.060) (0.059) Fintech Nbr. Patents 0.001 -0.000 0.000 -0.001 (0.005) (0.005) (0.006) (0.005) Fintech Age -0.005 -0.006 -0.021* -0.022* (0.004) (0.005) (0.010) (0.011) Country Dummies Yes Yes Yes Yes N (Banks) 331 346 282 295 Pseudo ar2 0.273 0.285 0.268 0.285 Wald chi2 51362 58935 57.687 58.797 Prob > chi2 0.000 0.000 0.000 0.000
37
Table 6 Cumulative Average Abnormal Returns (CAAR) for bank-fintech alliances. This table reports descriptive statistics of CARs for various event windows, based on 140 alliances done by all the 21 publicly listed banks in our sample for the period from 2007 to 2017. Daily Abnormal Returns are obtained using the market model with a 200-trading day window, ending 20 days before the event day to avoid bias in the parameters estimations due to changes in firm characteristics around the event date. * denotes significance at the 5% level, ** at the 1% level, and *** at the 0.1 % level. t-test Wilcoxon sign-rank
Event window CAAR (%) t-statistic z-statistic Minimum (%) Maximum (%) Percentage of
positive CAR (%) -1 to +1 -0.52 -2.050* -1.82 -7.25 6.10 43.69 -1 to 0 -0.53 -2.475* -2.538* -5.43 4.59 38.95 0 to +1 -0.18 -0.823 -0.928 -5.81 6.03 44.25
-3 to +3 -0.72 -1.893 -1.564 -12.06 8.25 42.70 -5 to +5 -0.25 -0.353 -0.021 -9.50 7.64 52.74
-10 to +10 -0.70 -1.117 1.591 -15.24 17.55 46.95 0 to 100 2.89 1.506 -1.082 -40.94 38.78 58.16
N 140
Impact of Alliance on Bank Value
21
38
Table 7 Determinants of shareholder value creation from bank-fintech alliances. This table provides results of ordinary least squares (OLS) regression, where the dependent variables are the standardized CARs for the selected event windows (-1;0), (0;+1), (-1;+1), and (0;+100). Standard errors shown in parentheses are clustered by banks. All variables are defined in Table 1. * denotes significance at the 5% level, ** at the 1% level, and *** at the 0.1 % level. Here, (d) indicates a dummy variable.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) bank variables bank and investment variables bank and fintech variables
Dependent Variable: CAR Event Windows -1 to +1 -1 to 0 0 to +1 0 to +100 -1 to +1 -1 to 0 0 to +1 0 to +100 -1 to +1 -1 to 0 0 to +1 0 to +100 Digital Strategy (d) -0.018 -0.014 -0.007 0.187* -0.019 -0.015 -0.007 0.193* -0.012 -0.01 -0.002 0.199* (0.011) (0.009) (0.009) (0.079) (0.011) (0.009) (0.009) (0.077) (0.011) (0.010) (0.009) (0.081) Chief Digital Officer (d) 0.005 0.002 -0.001 -0.055 0.007 0.001 0.001 -0.058 0.007 0.003 -0.002 -0.059
(0.006) (0.005) (0.005) (0.043) (0.006) (0.005) (0.005) (0.045) (0.007) (0.006) (0.005) (0.049) Majority Investment (d) 0.03 0.005 0.031* -0.004 -0.015 0.008 -0.011 -0.052
(0.017) (0.015) (0.014) (0.126) (0.013) (0.011) (0.010) (0.092) Chief Digital Officer x Majority Investment -0.040* 0.005 -0.037* -0.04 (0.020) (0.017) (0.016) (0.142) Universal Bank (d) 0.003 -0.001 0.001 -0.100 0.007 0.002 0.005 -0.106 0.000 0.003 -0.003 -0.166
(0.009) (0.008) (0.008) (0.070) (0.010) (0.009) (0.008) (0.073) (0.012) (0.010) (0.009) (0.084) Digital Bank (d) 0.032** 0.029** 0.025* 0.008 0.026* 0.026* 0.019 0.017 0.030* 0.024 0.029* 0.029
(0.012) (0.010) (0.010) (0.089) (0.012) (0.011) (0.010) (0.090) (0.015) (0.013) (0.012) (0.106) ln(Bank Age) -0.002 -0.001 -0.003 -0.005 -0.002 -0.002 -0.004* -0.009 -0.002 -0.001 -0.003 -0.007
(0.002) (0.002) (0.002) (0.019) (0.003) (0.002) (0.002) (0.019) (0.003) (0.003) (0.002) (0.021) ln(Bank Total Assets) 0.002 0.004 0.003 0.013 0.002 0.004 0.003 0.016 0.002 0.003 0.004 0.020
(0.002) (0.002) (0.002) (0.018) (0.003) (0.002) (0.002) (0.018) (0.003) (0.002) (0.002) (0.020) ROAA -0.358 0.013 -0.407 7.366 -0.252 0.126 -0.209 6.353 -0.060 0.403 -0.283 6.558
(0.550) (0.469) (0.451) (4.123) (0.567) (0.499) (0.457) (4.117) (0.666) (0.597) (0.529) (4.822) Fintech Front End Solution (d) -0.004 -0.002 -0.004 -0.043
(0.004) (0.004) (0.004) (0.032) Fintech Employees (rank) 0.001 0.000 0.001 -0.008
(0.002) (0.002) (0.002) (0.016) Fintech Headquarter (d) -0.002 -0.006 0.003 0.040
(0.005) (0.004) (0.004) (0.035) Fintech Number of Patents 0.000 0.000 0.000 -0.002
(0.000) (0.000) (0.000) (0.003) Fintech Age 0.003 0.001 0.003 0.002
(0.004) (0.003) (0.003) (0.025) Country Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 139 139 139 139 128 128 128 128 116 116 116 116 R2 0.105 0.094 0.152 0.126 0.135 0.105 0.207 0.131 0.14 0.135 0.17 0.188 P 0.143 0.225 0.016 0.06 0.134 0.347 0.006 0.157 0.453 0.502 0.231 0.141
Determinants of Bank Value Creation
22
23
Conclusion
Summary
24
Implementation of digitalization strategy by banks or employment of a Chief Digital Officer have positive effect on alliances with fintechs1
2
3
Large banks more willing to engage financially in fintech firms; banks more likely to invest in small rather than large fintechs à right step?
Announcements of alliances have negative effect on bank’s value; digital banks benefit from alliances à apparently not for all banks.
Thank you for your attention!
25
Prof. Dr. Lars Hornufwww.hornuf.com
