## Annex I. Variable Names, Definition and Sources

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---

### Introduction and scope
- Focus: impact of FinTech finance (digital lending and digital capital raising) on incumbent financial institutions’ (FIs) performance across 57 countries using 2012–2020 data.
- Definition of FinTech: “new business models, applications, processes, or products with an associated material effect on the provision of financial services” (FSB, 2017).
- Contextual facts:
  - FinTech currently estimated at around 2 percent of the total credit in major FinTech markets.
  - Allied Research (2021) projection: global FinTech lending industry to $4.9 trillion by 2030.
  - Investments in FinTech platforms: $217 billion in 2019 from $4 billion in 2012 (Statista, 2022).

### Data, sample, and econometric approach
- Datasets:
  - Country-level FinTech transactions: Cambridge Center for Alternative Finance (CCAF), 57 countries, yearly 2012–2020.
  - Bank-level data: 10,167 financial institutions from Bureau van Dijk Orbis (unconsolidated preferred).
  - Country macro and structural indicators: IMF WEO, World Bank Governance Indicators, World Development Indicators, Haver, Global Financial Development Database.
- Main dependent variables (definitions in Annex I):
  - ROE: Return on Equity (%) = Net Income/Equity.
  - ROA: Return on Assets (%) = Net Income/Assets.
  - NIM: Net Interest Margin (%) = Interest Income-Interest Exp/Interest-earning assets.
  - NONIC: Non-Interest Income to Average Assets (%).
  - CTI: Cost to Income ratio (%).
- Main explanatory variables:
  - FinTech: Log (Total volume of digital lending and capital raising activities in US$). Source: CCAF.
  - P2P Lending, Balance Sheet Lending: analogous logs from CCAF.
- Baseline specification:
  - PERb,c,t = α + β1 FinTechc,t + γ Xb,c,t + δ Wc,t + Otherb,c,t
- Hypotheses:
  - Complementarity: ∂ROE/∂FinTech > 0
  - Substitution: ∂ROE/∂FinTech < 0
- Variables winsorized at the 1 percent level.

### Main empirical findings (baseline coefficients and interpretation)
- Baseline negative impact supports the substitution hypothesis.
- Point estimates from Table 2:
  - FinTech → ROE: -0.0903*** (0.0113)
  - FinTech → ROA: -0.0246*** (0.00233)
  - FinTech → NIM: -0.0277*** (0.00340)
  - FinTech → NONIC: 0.0111*** (0.00338)
  - FinTech → CTI: 0.136*** (0.0292)
- Magnitude interpretation preserved:
  - A 1 percentage point increase in FinTech transaction volumes → reduction of 0.09 percentage points in ROE and 0.02 percentage points in ROA.
  - A 1 percentage point increase in FinTech → decrease in NIM by 0.03 percentage points.
  - A 1 percentage point increase in FinTech → increase in CTI by 0.14 percentage points.
  - A 1 percentage point increase in FinTech → increase in NONIC by 0.01 percentage points (small relative to median NONIC = 1.99 percent).
- Sample medians:
  - Median ROE = 4.2 percent
  - Median ROA = 0.5 percent
  - Median NONIC = 1.99 percent

### Effects by FinTech business model and bank type (selected Table 3 results)
- Aggregate FinTech (restate baseline):
  - ROE -0.0903***; ROA -0.0246***; NIM -0.0277***; NONIC 0.0111***; CTI 0.136***.
- Cooperative banks (P2P vs Balance Sheet):
  - P2P lending → ROE: -0.333*** (0.0477); ROA: -0.128*** (0.0103); NIM: -0.111*** (0.0216); NONIC: 0.0192 (0.0141); CTI: 0.676*** (0.128)
  - Balance Sheet lending → ROE: -0.192*** (0.0323); ROA: -0.0910*** (0.00761); NIM: -0.142*** (0.0125); NONIC: 0.0177* (0.00939); CTI: -0.0944 (0.0851)
  - Interpretation: cooperative banks particularly susceptible; effects driven by reduced NIM and higher CTI.
- Commercial banks:
  - P2P lending → ROE: -0.0489 (0.0467); ROA: -0.0153 (0.0105); NIM: 0.00653 (0.0239); NONIC: 0.0611*** (0.0165); CTI: 0.286** (0.119)
  - Balance Sheet lending → ROE: -0.0749 (0.0756); ROA: -0.0120 (0.0145); NIM: -0.0612*** (0.0228); NONIC: 0.0225 (0.0320); CTI: 0.449*** (0.149)
  - Interpretation: commercial banks show insignificant ROE/ROA impacts overall; P2P associated with higher NONIC for commercial banks; Balance Sheet lending negatively affects commercial bank NIM.

### Heterogeneity by country- and bank-specific characteristics (selected interaction net effects)
- Country-level interactions (FinTech * characteristic → interaction; Net Effect on ROE where reported):
  - FinTech * Low Concentration → interaction: -0.0704***; Net Effect: -0.1041 (ROE)
  - FinTech * High Stock Market Turnover → interaction: -0.0418*; Net Effect: -0.1215 (ROE)
  - FinTech * High Credit depth → interaction: -0.14***; Net Effect: -0.2079 (ROE)
  - FinTech * High ROE (financial system) → interaction: -0.0592***; Net Effect: -0.1274 (ROE)
  - FinTech * High Regulatory quality → interaction: 0.316**; Net Effect: 0.2235 (ROE)
  - FinTech * High Government effectiveness → interaction: 1.513***; Net Effect: 1.4175 (ROE)
- Bank-level interactions:
  - FinTech * Low NPL → interaction: -0.157***; Net Effect: -0.2386 (ROE)
  - FinTech * High Risk-taking (Z-Score) → interaction: -0.0636***; Net Effect: -0.1634 (ROE)
  - FinTech * High Capital → interaction: -0.119**; Net Effect: -0.0815 (ROE)
- Interpretation preserved:
  - FinTech activity gravitates to more competitive, profitable, and developed financial systems (low concentration, high stock turnover, high credit depth, high ROE).
  - Strong regulatory quality and government effectiveness reverse the sign: incumbents benefit from increased FinTech penetration under robust regulatory institutions (positive net effects reported).
  - Incumbents with lower risk profiles (lower NPLs, higher capital, lower insolvency probability) experience more adverse profitability impacts from FinTech presence.

### Robustness checks (selected results)
- Alternative scalings:
  - FinTech_assets → ROE: -0.321** (0.153); ROA: -0.177*** (0.0365); NIM: -0.300*** (0.0595); NONIC: 0.172*** (0.0418); CTI: 0.482 (0.382)
  - FinTech_gdp → ROE: -0.144 (0.160); ROA: -0.221*** (0.0381); NIM: -0.353*** (0.0515); NONIC: 0.210*** (0.0486); CTI: 0.340 (0.428)
  - FinBigTech → ROE: -0.0898*** (0.0113); ROA: -0.0245*** (0.00234); NIM: -0.0259*** (0.00338); NONIC: 0.0110*** (0.00342); CTI: 0.130*** (0.0293)
- Instrumental variables / endogeneity (2SLS):
  - IV: Internet Penetration → FinTech: ROE -0.119*** (0.0156); ROA -0.0624*** (0.00310); NIM -0.0560*** (0.00508); NONIC 0.0376*** (0.00385); CTI 0.00174 (0.0331)
  - IV: FinTech(-c) (other countries) → ROE -0.136*** (0.00909); ROA -0.0308*** (0.00181); NIM -0.0227*** (0.00297); NONIC 0.00622*** (0.00226); CTI 0.0719*** (0.0193)
  - IV: Institutional Funding excluding country → ROE -0.550*** (0.0734); ROA -0.212*** (0.0152); NIM -0.194*** (0.0248)
- Dynamic and panel variants:
  - Two-step GMM: FinTech → ROE -0.274*** (0.0467); ROA -0.0631*** (0.0132); NIM -0.0623** (0.0254); NONIC 0.0228* (0.0129); CTI 0.190** (0.0924)
  - Balanced panel: FinTech → ROE -0.0674*** (0.0112); ROA -0.00705*** (0.00218); NIM -0.0143*** (0.00337); CTI 0.114*** (0.0228)
  - Lagged FinTech (-1): ROE -0.0471*** (0.0114); ROA -0.0212*** (0.00239); NIM -0.0163*** (0.00317); NONIC 0.00879*** (0.00322)
- Sensitivity to large-country exclusion:
  - Excluding China: FinTech → ROE -0.0946*** (0.0115); ROA -0.0254*** (0.00238); NIM -0.0278*** (0.00344); NONIC 0.0111*** (0.00344); CTI 0.138*** (0.0298)

### Interpretation of mechanisms
- Substitution channels supported:
  - FinTech competition lowers incumbents’ interest income (NIM) and raises costs (CTI), yielding lower ROE and ROA.
- Complementarity channel limited:
  - Small positive effect on NONIC indicates incumbents attempt to diversify into fee-based income, but gains insufficient to offset interest income losses and higher costs.
- Business-model heterogeneity:
  - P2P can be complementary in partnership cases (commercial banks’ NONIC).
  - Balance Sheet lending more substitutive (direct competition affecting NIM).

### Conclusions and policy recommendations (preserved framing)
- Core conclusion: greater FinTech presence adversely impacts incumbent FI profitability overall, consistent with substitution effects driven by lower interest income and higher costs.
- Bank-type implications:
  - Cooperative banks: particularly vulnerable; may require targeted support to build digital capacity and operational efficiency.
  - Commercial banks: better positioned; may benefit from partnering with P2P platforms to boost non-interest income.
- Country/institution implications:
  - FinTech tends to flourish in more competitive, profitable, and developed financial systems.
  - Strong regulatory quality and government effectiveness can produce positive net effects for incumbents—suggesting well-designed regulation fosters a level playing field.
- Policy recommendations:
  - Review and redesign licensing regimes to encompass new types of service providers within the regulatory framework where appropriate.
  - Implement capital, liquidity, and operational risk management requirements aligned to risks from different FinTech business models.
  - Strengthen regulatory framework and supervision for smaller, less technologically advanced incumbents who may be more vulnerable to FinTech competition.
  - Incumbents: enhance cost efficiency, diversify income sources, consolidate operations, improve internal governance, and address problem loans.

### Annex I — Variable definitions (selected)
- ROE: Return on Equity (%) = Net Income/Equity. Source: Bureau van Dijk Orbis
- ROA: Return on Assets (%) = Net Income/Assets. Source: Bureau van Dijk Orbis
- NIM: Net Interest Margin (%) = Interest Income-Interest Exp/Interest-earning assets. Source: Bureau van Dijk Orbis
- NONIC: Non-Interest Income to Average Assets (%) = Non-Interest Income/Average Assets. Source: Bureau van Dijk Orbis
- CTI: Cost to Income ratio (%) = Operating Exp./Operating Income-Non-Operating Income. Source: Bureau van Dijk Orbis
- FinTech: Log (Total volume of digital lending and capital raising activities in US$). Source: Cambridge Center for Alternative Finance

### Annex II — Selected descriptive statistics and correlations (preserved figures)
- LFintech: Obs 87384; Mean 20.896; Median 22.205; Std. Dev. 4.089; Min 7.24; Max 26.6
- ROE (bank-level series): Obs 84311; Mean 4.901; Median 4.249; Std. Dev. 9.217; Min -47.664; Max 50.971
- ROA: Obs 84565; Mean .812; Median 0.488; Std. Dev. 2.145; Min -9.532; Max 14.261
- NIM: Obs 84274; Mean 4.972; Median 3.131; Std. Dev. 7.117; Min -1.656; Max 59.484
- NONIC: Obs 84530; Mean 1.966; Median 0.989; Std. Dev. 5.175; Min -.607; Max 55.061
- CTI: Obs 84220; Mean 76.844; Median 78.399; Std. Dev. 25.177; Min 5.157; Max 216.483
- Key correlations with lfintech:
  - ROE: -0.080* (p-value (0.000))
  - ROA: -0.102* (p-value (0.000))
  - NIM: -0.158* (p-value (0.000))
  - NONIC: -0.087* (p-value (0.000))
  - CTI: 0.152* (p-value (0.000))

*Source: wpiea2023239-print-pdf - Annex I. Variable Names, Definition and Sources.*

### Annex I. Variable Names, Definition and Sources ........................................................................

### Annex I. Variable Names, Definition and Sources

### Document structure and adjacent annexes
- Annex I. Variable Names, Definition and Sources ......................................................................................... 24
- Annex II. Descriptive statistics, correlations, and stylized facts .................................................................. 25
- Annex III. List of Countries included in the Sample ...................................................................................... 28
- Annex IV. Digital lending and capital raising activities ................................................................................. 30
- Annex V. Emergence of Fintech Transactions Worldwide: Key Stylized Facts .......................................... 31
- Annex VI. Detailed Regression Output Tables ............................................................................................... 32

*Source: wpiea2023239-print-pdf - Annex I. Variable Names, Definition and Sources.*

### References .............................................................................................................

### wpiea2023239-print-pdf - References .............................................................................................................

### Introduction and scope
- Focus: impact of FinTech finance (digital lending and digital capital raising) on incumbent financial institutions’ (FIs) performance across 57 countries using 2012–2020 data.
- Definition of FinTech used: “new business models, applications, processes, or products with an associated material effect on the provision of financial services” (FSB, 2017).
- Contextual facts preserved from source:
  - FinTech currently estimated at around 2 percent of the total credit in major FinTech markets.
  - Allied Research (2021) projection: global FinTech lending industry to $4.9 trillion by 2030.
  - Investments in FinTech platforms: $217 billion in 2019 from $4 billion in 2012 (Statista, 2022).

### Data, sample, and econometric approach
- Datasets combined:
  - Country-level FinTech transactions from Cambridge Center for Alternative Finance (CCAF), 57 countries, yearly 2012–2020.
  - Balance sheet and income data for 10,167 financial institutions from Bureau van Dijk Orbis (unconsolidated preferred; consolidated used when unconsolidated unavailable).
  - Country macro and structural indicators from IMF WEO, World Bank Governance Indicators, World Development Indicators, Haver, Global Financial Development Database.
- Main dependent variables: ROE, ROA, NIM, NONIC (Non-Interest Income), CTI (Cost-to-Income). Variables winsorized at the 1 percent level.
- Baseline regression specification (parsimony preserved): 
  - PERb,c,t = α + β1 FinTechc,t + γ Xb,c,t + δ Wc,t + Otherb,c,t
- Hypotheses:
  - Complementarity: ∂ROE/∂FinTech > 0
  - Substitution: ∂ROE/∂FinTech < 0
- Extensions: disaggregate FinTech by business model (P2P vs Balance Sheet), include interaction terms with country- and bank-specific characteristics ωb,c,t.

### Main empirical findings (baseline)
- Baseline negative impact of FinTech on incumbents’ profitability supports the substitution hypothesis.
- Point estimates from Table 2 (coefficients with statistical significance markers preserved):
  - FinTech → ROE: -0.0903*** (standard error (0.0113))
  - FinTech → ROA: -0.0246*** (0.00233)
  - FinTech → NIM: -0.0277*** (0.00340)
  - FinTech → NONIC: 0.0111*** (0.00338)
  - FinTech → CTI: 0.136*** (0.0292)
- Interpretation emphasized in source:
  - A 1 percentage point increase in FinTech transaction volumes → reduction of 0.09 percentage points in ROE and 0.02 percentage points in ROA.
  - A 1 percentage point increase in FinTech → decrease in NIM by 0.03 percentage points.
  - A 1 percentage point increase in FinTech → increase in CTI by 0.14 percentage points.
  - A 1 percentage point increase in FinTech → increase in NONIC by 0.01 percentage points (small relative to median NONIC = 1.99 percent).
- Sample medians reported:
  - Median ROE = 4.2 percent
  - Median ROA = 0.5 percent
  - Median NONIC = 1.99 percent

### Effects by FinTech business model and bank type (summary of Table 3)
- All FinTech (aggregate): ROE -0.0903***; ROA -0.0246***; NIM -0.0277***; NONIC 0.0111***; CTI 0.136***.
- Cooperative banks:
  - P2P lending → ROE: -0.333***; ROA: -0.128***; NIM: -0.111***; NONIC: 0.0192; CTI: 0.676***
  - Balance Sheet lending → ROE: -0.192***; ROA: -0.0910***; NIM: -0.142***; NONIC: 0.0177*; CTI: -0.0944
  - Interpretation: cooperative banks particularly susceptible; effects driven by reduced NIM and higher CTI.
- Commercial banks:
  - P2P lending → ROE: -0.0489; ROA: -0.0153; NIM: 0.00653; NONIC: 0.0611***; CTI: 0.286**
  - Balance Sheet lending → ROE: -0.0749; ROA: -0.0120; NIM: -0.0612***; NONIC: 0.0225; CTI: 0.449***
  - Interpretation: commercial banks show insignificant ROE/ROA impacts overall; P2P associated with higher NONIC for commercial banks (suggesting benefits from partnerships); Balance Sheet lending negatively affects commercial bank NIM.

### Heterogeneity by country- and bank-specific characteristics (summary of Table 4)
- Country-level interaction findings (coefficients and net effects preserved as reported):
  - FinTech * Low Concentration → interaction: -0.0704***; Net Effect: -0.1041 (ROE)
    - Corresponding ROA figures: interaction -0.0021; Net Effect -0.0258
  - FinTech * High Stock Market Turnover → interaction: -0.0418*; Net Effect: -0.1215 (ROE)
    - ROA interaction -0.00646; Net Effect -0.03096
  - FinTech * High Credit depth → interaction: -0.14***; Net Effect: -0.2079 (ROE)
    - ROA interaction -0.0196***; Net Effect -0.0428
  - FinTech * High ROE (financial system) → interaction: -0.0592***; Net Effect: -0.1274 (ROE)
    - ROA interaction -0.0164***; Net Effect -0.0378
  - FinTech * High Regulatory quality → interaction: 0.316**; Net Effect: 0.2235 (ROE)
    - ROA interaction 0.0878*; Net Effect 0.0629
  - FinTech * High Government effectiveness → interaction: 1.513***; Net Effect: 1.4175 (ROE)
    - ROA interaction 0.144***; Net Effect 0.1188
- Bank-level interactions:
  - FinTech * Low NPL → interaction: -0.157***; Net Effect: -0.2386 (ROE)
    - ROA interaction -0.057***; Net Effect -0.0784
  - FinTech * High Risk-taking (Z-Score) → interaction: -0.0636***; Net Effect: -0.1634 (ROE)
    - ROA interaction -0.035***; Net Effect -0.0584
  - FinTech * High Capital → interaction: -0.119**; Net Effect: -0.0815 (ROE)
    - ROA interaction -0.0314***; Net Effect -0.0212
- Interpretation preserved from source:
  - FinTech activity is attracted to more competitive, profitable, and developed financial systems (low concentration, high stock turnover, high credit depth, high ROE).
  - Strong regulatory quality and government effectiveness reverse the sign: incumbents benefit from increased FinTech penetration under robust regulatory institutions (positive net effects reported).
  - Incumbents with lower risk profiles (lower NPLs, higher capital, lower insolvency probability) experience more adverse profitability impacts from FinTech presence.

### Robustness checks (summary of Table 5 and narrative)
- Alternative scalings and definitions:
  - FinTech_assets → ROE: -0.321**; ROA: -0.177***; NIM: -0.300***; NONIC: 0.172***; CTI: 0.482
  - FinTech_gdp → ROE: -0.144; ROA: -0.221***; NIM: -0.353***; NONIC: 0.210***; CTI: 0.340
  - FinBigTech (combined FinTech + BigTech) → ROE: -0.0898***; ROA: -0.0245***; NIM: -0.0259***; NONIC: 0.0110***; CTI: 0.130***
- Instrumental variables / endogeneity approaches (2SLS):
  - FinTech instrumented by Internet Penetration (IP) → ROE: -0.119***; ROA: -0.0624***; NIM: -0.0560***; NONIC: 0.0376***; CTI: 0.00174
  - FinTech instrumented by FinTech(-c) (sum leaving out country c) → ROE: -0.136***; ROA: -0.0308***; NIM: -0.0227***; NONIC: 0.00622***; CTI: 0.0719***
  - FinTech instrumented by Institutional Funding excluding country (IF) → ROE: -0.550***; ROA: -0.212***; NIM: -0.194***; NONIC: 0.0272; CTI: 0.0561
- Two-step GMM:
  - FinTech → ROE: -0.274***; ROA: -0.0631***; NIM: -0.0623**; NONIC: 0.0228*; CTI: 0.190**
- Other robustness variants:
  - Balanced panel FinTech → ROE: -0.0674***; ROA: -0.00705***; NIM: -0.0143***; NONIC: 0.00106; CTI: 0.114***
  - Lagged FinTech (-1) → ROE: -0.0471***; ROA: -0.0212***; NIM: -0.0163***; NONIC: 0.00879***; CTI: -0.0425
  - Adding Covid dummy (2020) → FinTech: -0.0900*** (ROE); -0.0230*** (ROA); -0.0242*** (NIM); 0.00825*** (NONIC); 0.114*** (CTI)
  - Excluding China, US, UK: FinTech_exclCHN → ROE: -0.0946***; ROA: -0.0254***; NIM: -0.0278***; NONIC: 0.0111***; CTI: 0.138***

### Interpretation of mechanisms (as presented)
- Substitution channels supported: FinTech competition lowers incumbents’ interest income (NIM) and raises costs (CTI), yielding lower ROE and ROA.
- Complementarity channel limited: small positive effect on NONIC indicates incumbents attempt to diversify into fee-based income, but gains insufficient to offset interest income losses and higher costs.
- Business-model heterogeneity: P2P more complementary in some partnership cases (commercial banks’ NONIC), Balance Sheet lending more substitutive (direct competition affecting NIM).

### Conclusions and policy implications (preserved framing and recommendations)
- Main conclusion: greater FinTech presence adversely impacts incumbent FI profitability overall, consistent with substitution effects driven by lower interest income and higher costs.
- Bank-type implications:
  - Cooperative banks: particularly vulnerable; may require targeted support to build digital capacity and operational efficiency.
  - Commercial banks: better positioned; may benefit from partnering with P2P platforms to boost non-interest income.
- Country/institution implications:
  - FinTech tends to flourish in more competitive, profitable, and developed financial systems.
  - Strong regulatory quality and government effectiveness can produce positive net effects for incumbents—suggesting well-designed regulation fosters a level playing field.
- Policy recommendations explicitly preserved from source:
  - Review and redesign licensing regimes to encompass new types of service providers within the regulatory framework where appropriate.
  - Implement more robust capital, liquidity, and operational risk management requirements that match the risks posed by different FinTech business models.
  - Strengthen regulatory framework and supervision for smaller, less technologically advanced incumbents who may be more vulnerable to FinTech competition.
  - Incumbents: enhance cost efficiency, diversify income sources, consolidate operations, improve internal governance, and address problem loans.

*Source: wpiea2023239-print-pdf - References .............................................................................................................*

### Annex I. Variable Names, Definition and Sources

### Annex I. Variable Names, Definition and Sources

### Dependent variables
- ROE: Return on Equity (%) = Net Income/Equity. Source: Bureau van Dijk Orbis
- ROA: Return on Assets (%) = Net Income/Assets. Source: Bureau van Dijk Orbis
- NIM: Net Interest Margin (%) = Interest Income-Interest Exp/Interest-earning assets. Source: Bureau van Dijk Orbis
- NONIC: Non-Interest Income to Average Assets (%) = Non-Interest Income/Average Assets. Source: Bureau van Dijk Orbis
- CTI: Cost to Income ratio (%) = Operating Exp./Operating Income-Non-Operating Income. Source: Bureau van Dijk Orbis

### Explanatory variables
- FinTech: Log (Total volume of digital lending and capital raising activities in US$). Source: Cambridge Center for Alternative Finance
- P2P Lending: Log (Total volume of P2P lending activities in US$). Source: Cambridge Center for Alternative Finance
- Balance Sheet Lending: Log (Total volume of Balance Sheet lending activities in US$). Source: Cambridge Center for Alternative Finance

### Other control variables
- Size: Log (Total Assets). Source: Bureau van Dijk Orbis
- Equity-Assets ratio: Equity to Total Assets (%). Source: Bureau van Dijk Orbis
- GDP growth: GDP, at constant prices, percent change (%). Source: IMF WEO Database
- Inflation: Annual percentage of average consumer prices (%). Source: IMF WEO Database
- Policy rate: Central Bank Policy rate (%). Source: Haver Database
- Bank concentration: Assets of five largest banks to total bank assets. Source: Global Financial Development Database
- NPL: Non-Performing Loans to Gross Loans (%). Source: Bureau van Dijk Orbis
- Total Capital Ratio: Total Capital Ratio (%). Source: Bureau van Dijk Orbis
- Risk-taking Z-Score: Distance from insolvency: (ROA+E/A)/s(ROA), where s(ROA) is the standard deviation of ROA. Source: Authors calculations using Bureau van Dijk Orbis data
- Stock Market Turnover Ratio: Total value of shares traded during the period divided by the average market capitalization for the period. (%). Source: Global Financial Development Database
- Private credit by deposit money banks to GDP (%): The financial resources provided to the private sector by domestic money banks as a share of GDP. Source: Global Financial Development Database
- Bank return on equity (%, after tax): Commercial banks’ after-tax net income to yearly averaged equity. Source: Global Financial Development Database
- Regulatory quality: Ability of government to implement sound policies that promote private sector development - Percentile rank (0-100). Source: World Governance Indicator
- Government effectiveness: Quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies - Percentile rank (0-100). Source: World Governance Indicator
- Internet penetration: Individuals using the Internet (% of population). Source: World Governance Indicator
- Institutional funding for FinTech platforms: The funding of FinTech platforms by institutional investors to support investment strategies or portfolio diversification for themselves or their clients. Source: Cambridge Center for Alternative Finance

### Annex II — Descriptive statistics (selected key statistics)
- LFintech: Obs 87384; Mean 20.896; Median 22.205; Std. Dev. 4.089; Min 7.24; Max 26.6
- ROE (bank-level series): Obs 84311; Mean 4.901; Median 4.249; Std. Dev. 9.217; Min -47.664; Max 50.971
- ROA: Obs 84565; Mean .812; Median 0.488; Std. Dev. 2.145; Min -9.532; Max 14.261
- NIM: Obs 84274; Mean 4.972; Median 3.131; Std. Dev. 7.117; Min -1.656; Max 59.484
- NONIC: Obs 84530; Mean 1.966; Median 0.989; Std. Dev. 5.175; Min -.607; Max 55.061
- CTI: Obs 84220; Mean 76.844; Median 78.399; Std. Dev. 25.177; Min 5.157; Max 216.483
- lTotalAssets: Obs 84897; Mean 11.563; Median 11.395; Std. Dev. 2.653; Min -.983; Max 22.106
- Equity to Total Assets: Obs 84831; Mean 16.143; Median 11.569; Std. Dev. 17.071; Min -971.677; Max 101.308
- NonPerfLoansGrossLoans: Obs 27543; Mean 8.953; Median 3.495; Std. Dev. 24.63; Min 0; Max 984.481
- Z-Score: Obs 74277; Mean 1.364e+13; Median 64.685; Std. Dev. 3.717e+15; Min -42.781; Max 1.013e+18
- Total Capital Ratio: Obs 21628; Mean 141.725; Median 18.230; Std. Dev. 16120.203; Min -5240.14; Max 2369797
- Stock Turnover: Obs 78449; Mean 106.341; Median 108.513; Std. Dev. 57.622; Min .27; Max 480.287
- Private credit to GDP: Obs 90171; Mean 140.598; Median 175.676; Std. Dev. 58.484; Min 10.247; Max 258.45
- Regulatory Quality: Obs 91503; Mean 80.214; Median 87.678; Std. Dev. 18.269; Min 13.942; Max 100
- Government Effectiveness: Obs 91503; Mean 81.015; Median 90.521; Std. Dev. 18.571; Min 10.577; Max 100
- GDP Growth: Obs 91503; Mean 1.469; Median 2.161; Std. Dev. 2.673; Min -11.115; Max 25.305
- Inflation: Obs 91335; Mean 2.36; Median 1.812; Std. Dev. 3.003; Min -2.074; Max 53.548
- Policy Rate: Obs 88663; Mean 2.258; Median 0.630; Std. Dev. 4.256; Min -.75; Max 59.25
- Bank Concentration: Obs 90992; Mean 59.318; Median 47.614; Std. Dev. 18.117; Min 31.855; Max 100

### Annex II — Correlations (selected coefficients and significance)
- Correlations with lfintech:
  - ROE: -0.080* (p-value (0.000))
  - ROA: -0.102* (p-value (0.000))
  - NIM: -0.158* (p-value (0.000))
  - NONIC: -0.087* (p-value (0.000))
  - CTI: 0.152* (p-value (0.000))
- Other notable correlations (coefficient followed by p-value in parentheses where provided):
  - ROA with ROE: 0.705* (0.000)
  - NIM with ROA: 0.346* (0.000)
  - CTI with ROE: -0.532* (0.000)
  - Stock Market Turnover with lfintech: 0.259* (0.000)
  - Private Credit to GDP with lfintech: 0.552* (0.000)
  - Regulatory Quality with lfintech: 0.296* (0.000)
  - Government Effectiveness with lfintech: 0.312* (0.000)
  - Bank Concentration with lfintech: -0.394* (0.000)
- Variables with weak or no significant correlation with lfintech in the table:
  - Z-Score with lfintech: -0.001 (p-value (0.701))
  - Total Capital Ratio with lfintech: 0.002 (p-value (0.758))

### Table 3 — Stylized facts by financial institution (medians and counts)
- Banks:
  - Commercial Bank: # of institutions 1409; Total Assets(th) median 985,502; ROE median 7.094; ROA median .8; NIM median 3.358858; NONIC median 1.105514; CTI median 59.90551
  - Cooperative Bank: # of institutions 7151; Total Assets(th) median 481,23.16; ROE median 3.818; ROA median .436; NIM median 3.178233; NONIC median .9632227; CTI median 81.47331
  - Islamic Bank: # of institutions 33; Total Assets(th) median 2,485,177; ROE median 6.555; ROA median .6215; NIM median 1.894553; NONIC median 1.860511; CTI median 63.95508
  - Micro-financing Institution: # of institutions 37; Total Assets(th) median 186,531; ROE median 10.5225; ROA median 2.294; NIM median 14.79198; NONIC median 2.270579; CTI median 67.11605
  - Savings Bank: # of institutions 568; Total Assets(th) median 576,127.8; ROE median 3.72; ROA median .4065; NIM median 2.102961; NONIC median .9446083; CTI median 73.67054
- Non-Banks:
  - Finance Company: # of institutions 516; Total Assets(th) median 427,016.2; ROE median 7.301; ROA median 1.0655; NIM median 4.159253; NONIC median 1.865802; CTI median 60.68638
  - Investment Bank: # of institutions 176; Total Assets(th) median 1,136,596; ROE median 5.783; ROA median .7455; NIM median 1.129579; NONIC median 2.373146; CTI median 68.23625
  - Investment and Trust Corporation: # of institutions 62; Total Assets(th) median 1230536; ROE median 6.2945; ROA median .816; NIM median .7153499; NONIC median 7.212011; CTI median 55.10713
  - Real Estate and Mortgage Bank: # of institutions 117; Total Assets(th) median 2418287; ROE median 5.1785; ROA median .331; NIM median 1.256582; NONIC median .0639484; CTI median 45.77802
  - Specialized Government Credit Institution: # of institutions 70; Total Assets(th) median 7,956,728; ROE median 4.73; ROA median .3505; NIM median .9904929; NONIC median .4957385; CTI median 41.58323

### Annex III — List of Countries included in the Sample (top entries by Total Alternative Finance Volume 2012-2020)
- 1. China: Total Alternative Finance Volume 2012-2020 (in billions of US$) 1,018.0; Total Alternative Finance Volume (% of GDP) 6.9
- 2. United States: 315.7; 1.5
- 3. United Kingdom: 58.0; 2.2
- 4. Brazil: 7.7; 0.5
- 5. Netherlands: 6.1; 0.7
- 6. France: 5.8; 0.2
- 7. Germany: 5.8; 0.2
- 8. Australia: 5.7; 0.4
- 9. India: 5.6; 0.2
- 10. Korea, Rep.: 5.2; 0.3
- (Full list continues through #57 Nigeria: 0.1; 0.0)

### Annex IV — Digital lending and capital raising activities (definitions)
- Digital lending activities:
  - P2P/Marketplace Lending: Individuals or institutional funders provide a loan to a consumer borrower, business borrower, or secured against a property, commonly ascribed to off-balance sheet lending.
  - Balance Sheet Lending: The platform entity provides a loan directly to the consumer borrower, business borrower, or secured against a property, ascribed to on-balance sheet nonbank lending.
  - Invoice Trading: Individuals or institutional funders purchase invoices or receivables from a business at a discount.
  - Securities:
    - Debt-based: Individuals or institutional funders purchase debt-based securities, typically a bond or debenture, at a fixed interest rate.
    - Mini-bonds: Individuals or institutions purchase securities from companies in the form of an unsecured bond which is ‘mini’ because the issue size is much smaller than the minimum issue amount needed for a bond issued in institutional capital markets.
  - Consumer Purchase Finance/BNPL: A buy now/pay later payment facilitator or Store Credit solution.
- Digital capital raising activities:
  - Equity-based: Individuals or institutional funders purchase equity issued by a company; provide equity or subordinated debt financing for real estate; purchase securities from a company, such as shares or bonds, and share in the profits or royalties of the business.
  - Non-Investment based: Backers provide funding to individuals, projects or companies in exchange for non-monetary rewards or products. Donors provide funding to individuals, projects or companies based on philanthropic or civic motivations with no expectation of monetary or material. Interests and/or other profits are re-invested (forgoing the interest by donating) or provides microcredit at lower rates.
- Source for Annex IV definitions: Cambridge Center for Alternative Finance (2021)

### Annex V — Emergence of FinTech Transactions Worldwide: Key Stylized Facts (text summary)
- FinTech volumes grew up significantly until 2017 and have declined since, driven largely by the decrease in volume from China.
- Market developments in China and the rest of the world have followed different trajectories; local market developments and regulatory changes in China led to a considerable decline in volumes and its global market share.
- US and Canada, followed by the UK, became the largest regional alternative market in 2020.
- P2P lending stands out as the largest business model when considering China in the analysis.
- Excluding China, total alternative finance volumes show gradual growth driven by both P2P/Marketplace Lending and Balance Sheet Lending.
- Figures referenced: FinTech finance volumes (including and excluding China), Market share (including and excluding China), FinTech finance volumes by model (including and excluding China). Source: Authors calculations using CCAF (2021) Database.

*International Monetary Fund — Annex I, II, III, IV, V as provided in the source PDF.*

### Annex VI. Detailed Regression Output Tables

### Annex VI. Detailed Regression Output Tables

### I. Effect of FinTech Business Models on Cooperative Banks
- Table 1: Effect of P2P lending on performance measures of Cooperative Banks
  - P2P lending:
    - ROE: -0.333*** (0.0477)
    - ROA: -0.128*** (0.0103)
    - NIM: -0.111*** (0.0216)
    - NONIC: 0.0192 (0.0141)
    - CTI: 0.676*** (0.128)
  - Control highlights (coefficients with standard errors):
    - Size: ROE 4.905*** (0.419); ROA 1.161*** (0.0870); CTI -12.84*** (1.086)
    - Equity-Assets ratio: ROE 0.381*** (0.0478); CTI -0.602*** (0.108)
    - GDP growth: ROE 0.158*** (0.0141); CTI -0.478*** (0.0364)
    - Policy rate: ROE 0.287*** (0.0403); CTI -1.150*** (0.0905)
  - Sample and fit:
    - N: ROE 52026; ROA 52035; NIM 52030; NONIC 52039; CTI 52015
    - rho: ROE 0.770; ROA 0.881; NIM 0.936; NONIC 0.891; CTI 0.817

- Table 2: Effect of Balance Sheet lending on performance measures of Cooperative Banks
  - Balance Sheet lending:
    - ROE: -0.192*** (0.0323)
    - ROA: -0.0910*** (0.00761)
    - NIM: -0.142*** (0.0125)
    - NONIC: 0.0177* (0.00939)
    - CTI: -0.0944 (0.0851)
  - Control highlights:
    - Size: ROE 5.400*** (0.453); CTI -11.21*** (1.286)
    - Equity-Assets ratio: ROE 0.461*** (0.0544); CTI -0.630*** (0.122)
    - GDP growth: ROE 0.173*** (0.0160); CTI -0.515*** (0.0437)
    - Policy rate: ROE 0.117*** (0.0444); CTI -0.775*** (0.0813)
  - Sample and fit:
    - N: ROE 48047; ROA 48056; NIM 48053; NONIC 48060; CTI 48038
    - rho: ROE 0.793; ROA 0.892; NIM 0.948; NONIC 0.912; CTI 0.780

### II. Effect of FinTech Business Models on Commercial Banks
- Table 3: Effect of P2P lending on performance measures of Commercial Banks
  - P2P lending:
    - ROE: -0.0489 (0.0467)
    - ROA: -0.0153 (0.0105)
    - NIM: 0.00653 (0.0239)
    - NONIC: 0.0611*** (0.0165)
    - CTI: 0.286** (0.119)
  - Control highlights:
    - Size: ROE 2.151*** (0.703); CTI -7.790*** (2.240)
    - GDP growth: ROE 0.485*** (0.0483); CTI -0.412*** (0.116)
    - Concentration: ROE 0.0819*** (0.0228)
  - Sample and fit:
    - N: ROE 7762; ROA 7855; NIM 7809; NONIC 7842; CTI 7786
    - rho: ROE 0.659; ROA 0.682; NIM 0.788; NONIC 0.846; CTI 0.673

- Table 4: Effect of Balance Sheet lending on performance measures of Commercial Banks
  - Balance Sheet lending:
    - ROE: -0.0749 (0.0756)
    - ROA: -0.0120 (0.0145)
    - NIM: -0.0612*** (0.0228)
    - NONIC: 0.0225 (0.0320)
    - CTI: 0.449*** (0.149)
  - Control highlights:
    - Size: ROE 4.080*** (0.853); CTI -8.370** (3.542)
    - GDP growth: ROE 0.609*** (0.0643); CTI -0.577*** (0.142)
    - Concentration: ROE 0.0849** (0.0379)
  - Sample and fit:
    - N: ROE 4153; ROA 4220; NIM 4191; NONIC 4215; CTI 4179
    - rho: ROE 0.806; ROA 0.782; NIM 0.801; NONIC 0.889; CTI 0.744

### III. Effect of FinTech depending on selected country and bank-specific characteristics
- Table 5: FinTech and its interaction with lower bank concentration (ROE, ROA)
  - FinTech: ROE -0.0337 (0.0242); ROA -0.0237*** (0.00507)
  - Low concentration: ROE 1.415*** (0.471)
  - FinTech*Low concentration: ROE -0.0704*** (0.0204)
  - Controls: Size ROE 3.221*** (0.419); Equity-Assets ratio ROE 0.112*** (0.0215)
  - N: ROE 79949; ROA 80129; rho: ROE 0.679; ROA 0.776

- Table 6: FinTech and interaction with higher Stock Turnover Ratio
  - FinTech: ROE -0.0797*** (0.0124); ROA -0.0245*** (0.00258)
  - High Stock turnover: ROE 0.709 (0.476); ROA 0.159 (0.103)
  - FinTech*High Stock turnover: ROE -0.0418* (0.0218)
  - N: ROE 79523; ROA 79701; rho: ROE 0.675; ROA 0.777

- Table 7: FinTech and interaction with higher private credit to GDP
  - FinTech: ROE -0.0679*** (0.0120); ROA -0.0232*** (0.00250)
  - High credit to GDP: ROE 2.875*** (0.399); ROA 0.456*** (0.0834)
  - FinTech*High Credit to GDP: ROE -0.140*** (0.0178); ROA -0.0196*** (0.00364)
  - N: ROE 79523; ROA 79701; rho: ROE 0.679; ROA 0.779

- Table 8: FinTech and interaction with higher Commercial Bank’s ROE
  - FinTech: ROE -0.0682*** (0.0119); ROA -0.0214*** (0.00244)
  - High ROE: ROE 0.880** (0.436); ROA 0.333*** (0.0938)
  - FinTech*High ROE: ROE -0.0592*** (0.0195); ROA -0.0164*** (0.00410)
  - N: ROE 79523; ROA 79701; rho: ROE 0.672; ROA 0.774

- Table 9: FinTech and interaction with higher regulatory quality
  - FinTech: ROE -0.0925*** (0.0111); ROA -0.0249*** (0.00228)
  - High Regulatory Quality: ROE -6.936*** (2.068)
  - FinTech*High Regulatory Quality: ROE 0.316** (0.144); ROA 0.0878* (0.0532)
  - N: ROE 79523; ROA 79701; rho: ROE 0.681; ROA 0.776

- Table 10: FinTech and interaction with higher government effectiveness
  - FinTech: ROE -0.0955*** (0.0108); ROA -0.0252*** (0.00225)
  - High Government Effectiveness: ROE -21.14*** (2.986); ROA -2.036*** (0.556)
  - FinTech*High Government Effectiveness: ROE 1.513*** (0.207); ROA 0.144*** (0.0368)
  - N: ROE 79523; ROA 79701; rho: ROE 0.683; ROA 0.777

- Table 11: FinTech and interaction with lower NPLs of incumbents
  - FinTech: ROE -0.0816*** (0.0112); ROA -0.0214*** (0.00227)
  - Low NPLs: ROE 4.512*** (0.739); ROA 1.416*** (0.164)
  - FinTech*Low NPLs: ROE -0.157*** (0.0365); ROA -0.0570*** (0.00811)
  - N: ROE 79523; ROA 79701; rho: ROE 0.685; ROA 0.780

- Table 12: FinTech and interaction with higher solvency (High Z-Score)
  - FinTech: ROE -0.0998*** (0.0119); ROA -0.0234*** (0.00230)
  - High Z-Score: ROE 2.054*** (0.486); ROA 0.921*** (0.115)
  - FinTech*High Z-Score: ROE -0.0636*** (0.0210); ROA -0.0350*** (0.00482)
  - N: ROE 79523; ROA 79701; rho: ROE 0.688; ROA 0.784

- Table 13: FinTech and interaction with higher capital of incumbents
  - FinTech: ROE 0.0375 (0.0552); ROA 0.0102 (0.00842)
  - High capital: ROE 2.988*** (1.122); ROA 0.745*** (0.176)
  - FinTech*High capital: ROE -0.119** (0.0543); ROA -0.0314*** (0.00823)
  - N: ROE 79523; ROA 79754; rho: ROE 0.633; ROA 0.739

### IV. Robustness Checks
- Table 14: Effect of FinTech-Assets on incumbents (ROE, ROA, NIM, NONIC, CTI)
  - FinTech-Assets:
    - ROE: -0.321** (0.153)
    - ROA: -0.177*** (0.0365)
    - NIM: -0.300*** (0.0595)
    - NONIC: 0.172*** (0.0418)
    - CTI: 0.482 (0.382)
  - N: ROE 79523; ROA 79701; NIM 79438; NONIC 79666; CTI 79384
  - rho: ROE 0.660; ROA 0.763; NIM 0.911; NONIC 0.911; CTI 0.749

- Table 15: Effect of FinTech-GDP on incumbents
  - FinTech-GDP:
    - ROE: -0.144 (0.160)
    - ROA: -0.221*** (0.0381)
    - NIM: -0.353*** (0.0515)
    - NONIC: 0.210*** (0.0486)
    - CTI: 0.340 (0.428)
  - N and rho similar to Table 14

- Table 16: Effect of combined FinBigTech on incumbents
  - FinBigTech:
    - ROE: -0.0898*** (0.0113)
    - ROA: -0.0245*** (0.00234)
    - NIM: -0.0259*** (0.00338)
    - NONIC: 0.0110*** (0.00342)
    - CTI: 0.130*** (0.0293)
  - N: ROE 79948; ROA 80126; NIM 79861; NONIC 80089; CTI 79805
  - rho: ROE 0.684; ROA 0.776; NIM 0.909; NONIC 0.903; CTI 0.759

- Table 17: 2SLS using Internet Penetration as IV
  - FinTech:
    - ROE: -0.119*** (0.0156)
    - ROA: -0.0624*** (0.00310)
    - NIM: -0.0560*** (0.00508)
    - NONIC: 0.0376*** (0.00385)
    - CTI: 0.00174 (0.0331)
  - N: ROE 79496; ROA 79674; NIM 79411; NONIC 79639; CTI 79357
  - rho: ROE 0.688; ROA 0.805; NIM 0.910; NONIC 0.914; CTI 0.747

- Table 18: 2SLS using all other countries FinTech as IV
  - FinTech:
    - ROE: -0.136*** (0.00909)
    - ROA: -0.0308*** (0.00181)
    - NIM: -0.0227*** (0.00297)
    - NONIC: 0.00622*** (0.00226)
    - CTI: 0.0719*** (0.0193)
  - N: ROE 79523; ROA 79701; NIM 79438; NONIC 79666; CTI 79384
  - rho: ROE 0.693; ROA 0.781; NIM 0.910; NONIC 0.911; CTI 0.752

- Table 19: 2SLS using Regional Institutional Funding as IV
  - FinTech:
    - ROE: -0.550*** (0.0734)
    - ROA: -0.212*** (0.0152)
    - NIM: -0.194*** (0.0248)
    - NONIC: 0.0272 (0.0183)
    - CTI: 0.0561 (0.150)
  - N: ROE 56880; ROA 57016; NIM 56826; NONIC 56992; CTI 56786
  - rho: ROE 0.800; ROA 0.859; NIM 0.905; NONIC 0.910; CTI 0.767

- Table 20: Two-Step GMM Results
  - FinTech:
    - ROE: -0.274*** (0.0467)
    - ROA: -0.0631*** (0.0132)
    - NIM: -0.0623** (0.0254)
    - NONIC: 0.0228* (0.0129)
    - CTI: 0.190** (0.0924)
  - Year has coefficient in some specifications (e.g., Year 0.183*** (0.0409) for ROE)
  - N: ROE 79523; ROA 79701; NIM 79438; NONIC 79666; CTI 79384

- Table 21: Balanced Panel
  - FinTech:
    - ROE: -0.0674*** (0.0112)
    - ROA: -0.00705*** (0.00218)
    - NIM: -0.0143*** (0.00337)
    - NONIC: 0.00106 (0.00273)
    - CTI: 0.114*** (0.0228)
  - N: all dependent variables 54371
  - rho: ROE 0.606; ROA 0.659; NIM 0.900; NONIC 0.947; CTI 0.747

- Table 22: Lagged FinTech variable
  - L.FinTech:
    - ROE: -0.0471*** (0.0114)
    - ROA: -0.0212*** (0.00239)
    - NIM: -0.0163*** (0.00317)
    - NONIC: 0.00879*** (0.00322)
    - CTI: -0.0425 (0.0303)
  - N: ROE 71881; ROA 72040; NIM 71800; NONIC 72008; CTI 71751
  - rho: ROE 0.711; ROA 0.799; NIM 0.909; NONIC 0.919; CTI 0.767

- Table 23: Adding a COVID-19 dummy (Year 2020)
  - FinTech:
    - ROE: -0.0900*** (0.0110)
    - ROA: -0.0230*** (0.00227)
    - NIM: -0.0242*** (0.00313)
    - NONIC: 0.00825*** (0.00317)
    - CTI: 0.114*** (0.0280)
  - Covid dummy:
    - ROE: -0.0438 (0.302)
    - ROA: -0.240*** (0.0672)
    - NIM: -0.521*** (0.138)
    - NONIC: 0.430*** (0.0970)
    - CTI: 3.361*** (0.624)
  - N: ROE 79523; ROA 79701; NIM 79438; NONIC 79666; CTI 79384
  - rho: ROE 0.681; ROA 0.780; NIM 0.911; NONIC 0.912; CTI 0.763

- Table 24: Adding Time Fixed Effects with no macro controls
  - FinTech:
    - ROE: -0.0108 (0.0341)
    - ROA: -0.0442*** (0.00782)
    - NIM: -0.147*** (0.0146)
    - NONIC: 0.0342*** (0.0126)
    - CTI: 0.592*** (0.0823)
  - Year fixed effects listed for Year=2012 to Year=2020 (coefficients and s.e. provided)
  - N: ROE 82613; ROA 82799; NIM 82513; NONIC 82756; CTI 82461
  - rho: ROE 0.660; ROA 0.780; NIM 0.907; NONIC 0.907; CTI 0.755

- Tables 25–27: Excluding large-country observations
  - Table 25 (excluding China):
    - FinTech: ROE -0.0946*** (0.0115); ROA -0.0254*** (0.00238); NIM -0.0278*** (0.00344); NONIC 0.0111*** (0.00344); CTI 0.138*** (0.0298)
    - N: ROE 78487; ROA 78665; NIM 78405; NONIC 78631; CTI 78350
  - Table 26 (excluding China and US):
    - FinTech: ROE -0.101** (0.0495); ROA -0.0562*** (0.0113); NIM -0.0708*** (0.0214); NONIC 0.0615*** (0.0166); CTI 0.649*** (0.116)
    - N: ROE 30066; ROA 30224; NIM 30004; NONIC 30191; CTI 29965
  - Table 27 (excluding China, US, and UK):
    - FinTech: ROE -0.0984** (0.0499); ROA -0.0550*** (0.0114); NIM -0.0732*** (0.0215); NONIC 0.0608*** (0.0165); CTI 0.648*** (0.117)
    - N: ROE 29488; ROA 29644; NIM 29430; NONIC 29616; CTI 29398

*Source: Authors calculations*

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*Is FinTech Eating the Bank’s Lunch? Working Paper No. WP/2023/239*

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_Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023239-print-pdf.pdf_