## Fintech Competition and Banks’ Shrinking Margins in Brazil — INTRODUCTION

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### The Rise of Fintech in Brazil
- Nonbank credit companies nearly doubled from 2018 to 2024, becoming major competitors of banks.
- Four of the world’s ten largest digital banks are Brazilian.
- Nonbank credit companies account for a considerable portion of the unsecured consumer credit market, including 25 percent of the credit card sector.
- Active borrowers from these fintech companies rose from nearly zero to 60 million (about 40 percent of the adult population) between 2018 and 2023.
- Fintech entry reduced banking industry concentration and increased competition in the credit market.
- Fintech activity spans digital banking, currency exchange, lending, financial management, investments, insurance and payment systems.
- The analysis includes approximately 195 non-banking credit companies operating in Brazil as of end-2024.
- Nubank reached 100 million customers in Brazil by end-2024, of which 22 million accessed formal financial services for the first time.
- The number of active borrowers rose from 75 million in 2018 to 115 million in 2023, with most of the increase attributable to fintech companies.
- Fintech lender borrowers reached 60 million in 2023, representing half of all borrowers in the financial system.

### Drivers of Fintech Growth and Regulatory Milestones
- High cost of credit in Brazil: in 2023, average market lending rates exceeded funding rates by 30 percentage points.
- Global average spread for comparison: 5.9 percent.
- Primary drivers of high credit costs: bank profit margins and administrative expenses.
- Regulatory initiatives by the Central Bank of Brazil (BCB) since 2016 that facilitated fintech growth:
  - CMN Resolution 4,480, of April 25, 2016: permitted opening, maintaining, and closing deposit accounts exclusively through digital channels.
  - CMN Resolution 4,656, of April 26, 2018: defined authorization and modalities for credit fintechs, including SCDs and SEPs.
  - CMN Resolution 4,658, of April 26, 2018: requirements for contracting data processing and cloud computing services.
  - CMN Resolution 4,753, of September 26, 2019: enabled simplified digital relationship procedures proportionate to risk and nature of relationship.
  - BCB Resolution 29/2020 and BCB 50/2020: guidelines for a Regulatory Sandbox.
  - Pix instant payment system launched November 2020: 90 percent of adults use Pix and Pix transactions represent 49 percent of electronic payments in Brazil.
- Capital requirements: effective from January 2023 with progressive implementation until 2025; fintech conglomerates controlled by payment institutions but integrated with financial institutions face the same capital requirement as banks of similar scales.
- Digital banks comprised only 7% of total funding as of December 2024 (Financial Stability Report, 2025).
- Household debt-to-income ratio rose from 37% in 2018 to nearly 50% in 2022.
- Over 45% of consumers defaulted on financial obligations in 2023, compared to less than 40% in January 2018.
- The Desenrola program (started in July 2013) aided over 15 million people to renegotiate around R$50 billion in overdue debt by May 2024.

### Dataset and Empirical Strategy
- Data source and coverage:
  - Central Bank’s quarterly reports of financial institutions (IF database).
  - Coverage includes all authorized, normally operating financial institutions: about 100 commercial banks, 900 credit unions, and 300 non-banking credit companies and payment institutions.
  - Paper focuses on commercial banks, which account for 85 percent of total credit in the financial system.
  - Period: 2012 to 2024 with quarterly reports.
- Variable definitions and processing:
  - Net interest margin (NIM) = annualized net interest income over total assets.
  - Provision ratio = annualized provision expenses over total loans.
  - Cost of funding ratio = funding expense over total funding.
  - Average lending rates = income from credit operations divided by total loans.
  - For stock variables, a four-quarter moving average is used. Top and bottom 5 percent of observations trimmed for calculated ratios.
- Identification — Bartik (shift-share) exposure:
  - Exposure_{i,t} = sum over n of (Loan_share_{i,n} ⋅ Fintech_share_{n,t}).
  - Loan_share_{i,n} is bank i’s share of loan portfolio in credit segment n pre-fintech (average before 2017).
  - Fintech_share_{n,t} is the share of lending by fintech companies in credit segment n at t.
  - Exposure is standardized (number of standard deviations from the mean).
- Empirical specification:
  - y_{i,t} = α_{i} + β ⋅ Exposure_{i,t} + γ ⋅ X_{i,t} + δ_{year} ⋅ I_{year} + σ_{q} ⋅ I_{q} + ε_{i,t}.
  - Outcomes include average lending rates, funding cost ratio, net interest margin, cost to assets ratio, credit risk (NPL ratios and provisioning ratios), ROE, and ROA.
  - Controls: bank-specific controls (market share, liquid asset ratio), bank fixed effects, year fixed effects, quarter fixed effects; standard errors clustered at bank level.
- Exogeneity diagnostics:
  - Balance check: correlations between exposure and bank characteristics (including share of credit card loans) not significant.
  - Placebo test: no pre-trend in outcome variables before significant fintech competition.
  - Pre-existing loan composition fixed using average before 2017.

### Main Findings — Banks’ Responses to Fintech Competition
A. Price Effects
- A one standard deviation increase in fintech competition:
  - leads to a 3.7 percentage point reduction in the average lending rates offered by commercial banks (Table 4A, column 4).
  - compresses commercial banks’ NIM by 1.3 percentage points (Table 4A column 2).
- Contextual benchmarks:
  - Brazil’s average NIM was 4.4 percent in 2021.
  - The 3.7 percentage point reduction accounts for approximately one fifth of the interest rate spread observed between 2022 and 2024.

B. Quantity Effects
- Borrower counts and loan size:
  - A one standard deviation increase in fintech exposure is associated with a 32% reduction in borrowers (Table 4B, column 4).
  - A one standard deviation rise in fintech exposure corresponds to a 32% increase in loans per borrower (Table 4B column 6).
- Aggregate loan volume:
  - Commercial banks maintained overall loan portfolio size by increasing average loan size per customer and targeting high-value clients.
- Scale of fintech lending:
  - Fintech lender borrowers reached 60 million in 2023, representing half of all borrowers in the financial system.

C. Efficiency Effects
- A one standard deviation rise in fintech exposure leads to a 0.5 percentage point decrease in the administrative cost-to-asset ratio (Table 4C, column 2).
- This decrease represents approximately one third of the sample’s median cost-to-asset ratio.
- Structural context:
  - Approximately 24 percent of the interest rate spread is attributable to administrative expenses (BCB Financial Stability Report, 2025).
  - Banks accelerated digitalization, closed some physical branches, and opened digital branches since 2017; open finance initiatives and Pix supported digital transformation.

D. Risk Effects
- Credit risk measures (NPL ratios and provisioning ratio) show coefficients on exposure to fintech competition that are insignificant (Table C, column 3-6).
- Interpretation:
  - Fintech entry into unsecured consumer loan markets increased overall borrower risk profile (rising NPL ratios), but commercial banks shifted toward high-value, low-risk borrowers and better terms, leaving aggregate commercial bank credit risk largely stable.
- NPL ratios for credit card loans rose significantly during 2021-23, largely reflecting fintechs’ rapid expansion into higher-risk, first-time borrowers.
- Fintech competition produced no discernible effect on commercial banks' NPL ratios or provisioning expense ratios.

E. Profitability
- A one standard deviation rise in fintech exposure:
  - led to a 3.6 percentage point decrease in return on equity.
  - led to a 0.7 percentage point reduction in return on assets.
- Benchmarks:
  - Median return on equity is 9.1 percent.
  - Median return on assets is 1.1 percent.

F. Macro implications (aggregate effects since 2018)
- From 2018 to 2024 average fintech competition rose by 0.7 standard deviations.
- Applying model coefficients to this change implies:
  - 2.7 percentage point reduction in average lending rates.
  - 0.9 percentage point decrease in net interest margins.
  - 0.4 percentage point decline in administrative cost-to-asset ratio.
  - 0.5 percentage point reduction in return on assets.
- These represent:
  - 15 percent of the interest rate spread (as measured in 2024).
  - 20 percent of net interest margins (as measured in 2024).
  - 12 percent of the administrative cost-to-asset ratio (as measured in 2024).
  - 33 percent of the return on asset ratio (as measured in 2024).
- Macroeconomic context:
  - BCB initiated rate increases in 2022 and tightened policy from September 2024, raising the policy rate to 15 percent by June 2025, with real interest rates nearing 10 percent.
  - Despite tightening, credit growth remained strong, partly driven by fintech lenders’ expansion and competitive effects on banks.

### Robustness Checks and Alternative Estimators
- Alternative estimators considered:
  - Leave-one-out Bartik exposure.
  - Earlier base year (2016 instead of 2017) for pre-existing loan composition.
  - Two-stage-least-squares (2SLS) using Bartik instruments.
- Results:
  - Effects of fintech competition are robust to these alternative estimators (Table 5A).
- Controls for other structural changes:
  - Controlled for credit union presence and number of Pix users; effects of fintech competition remain significant (Table 5B).
  - Observed additional impacts:
    - Credit unions appear to raise lending rates and net interest margins at commercial banks.
    - Pix user numbers have little impact on bank performance.
- Structural developments accounted for:
  - Expansion of credit unions (majority prior to 2020; consolidation thereafter).
  - Pix adoption and market changes noted above.

### Conclusions and Policy Implications
- Causal effects:
  - Constructed bank-specific Bartik exposure to identify causal effects of fintech competition on incumbent banks.
  - Banks preserved loan portfolios by reducing lending rates and increasing loans per borrower.
  - Operational efficiency improved; overall profitability declined due to narrower interest margins.
- Quantified aggregate impact since 2018:
  - Decline in banks’ average lending rates by 2.7 percentage points.
  - Reduction in net interest margins by 0.9 percentage points.
- Fintechs and consumer credit expansion:
  - Fintechs now account for 25% of the credit card market and 12% of the non-payroll personal loan market.
  - The sector has reached 60 million borrowers, many previously lacking access to credit.
  - Fintech competition and incumbents’ rate reductions contributed to sustained consumer credit growth since 2022 despite restrictive monetary policy.
- Evolving competition and regulatory changes:
  - Payroll-backed loan reforms in March 2025 simplified access (centralized government app; direct payroll deductions and 10 percent of severance funds as collateral; bilateral employer–bank agreements no longer required), lowering entry barriers for fintech lenders.
  - Credit portability program being developed within Open Finance framework.
- Policy lessons and areas for future research:
  - Proportional regulation, specific licenses, and regulatory sandboxes supported fintech growth; BCB tightened regulation when fintechs reached scales similar to banks to ensure financial stability and a level playing field.
  - Rapid consumer credit expansion increased household indebtedness, creating scope to analyze macroprudential measures, stronger consumer protection laws, and financial education.

_Italic: Source — IMF Working Paper (Sections I–VI of the cited PDF)._

### INTRODUCTION _____________________________________________________________________ 3

### INTRODUCTION

### The Rise of Fintech in Brazil
- Nonbank credit companies nearly doubled from 2018 to 2024, becoming major competitors of banks.
- Four of the world’s ten largest digital banks are Brazilian.
- Nonbank credit companies account for a considerable portion of the unsecured consumer credit market, including 25 percent of the credit card sector.
- Active borrowers from these fintech companies rose from nearly zero to 60 million (about 40 percent of the adult population) between 2018 and 2023.
- Fintech entry reduced banking industry concentration and increased competition in the credit market.

### Drivers of Fintech Growth
- High cost of credit in Brazil: in 2023, average market lending rates exceeded funding rates by 30 percentage points.
- Global average spread for comparison: 5.9 percent.
- Primary drivers of high credit costs: bank profit margins and administrative expenses.
- Regulatory initiatives by the Central Bank of Brazil (BCB) since 2016: facilitation of credit portability, Pix instant payment system, progress in open finance, and targeted regulations to encourage new entrants.

### Empirical Strategy and Innovations
- Constructed a comprehensive dataset of key accounting and credit information for each financial institution regulated by BCB from 2013 to 2024.
- Developed a Bartik (shift-share) exposure metric:
  - A bank’s exposure to fintech competition = weighted sum of fintech market shares across loan segments, weights = bank’s initial exposure to each segment.
  - Exploits concentration of fintech activity in specific loan segments to generate cross-bank variation.
- Causal identification:
  - Relies on Goldsmith-Pinkham and others (2020) conditions: consistency if either shares are randomly distributed across banks or shocks are random across loan segments.
  - Standard diagnostic tests indicate initial loan shares are randomly allocated across banks in Brazil.
  - Panel regressions across commercial banks provide consistent estimates of fintech competition effects.

### Banks’ Responses and Main Findings
- Lending rates and margins:
  - A one standard deviation increase in fintech competition is associated with a 3.7 percentage point reduction in banks’ average lending rates.
  - The same shock is associated with a 1.3 percentage point decrease in net interest margins.
  - Since 2018, commercial banks lowered their lending rates by approximately 2.7 percentage points in response to increased fintech competition (calculated by multiplying the change in average fintech competition (2018-2024) with the panel regression coefficient).
- Profitability and efficiency:
  - Banks experienced decreased profitability alongside lower lending rates.
  - Incumbent banks improved operational efficiency by cutting administrative costs and developing online banking and digital services.
  - Banks introduced digital brands and partnerships with fintechs to improve user experience.
- Customer composition and credit volumes:
  - Competitive pressures led to a decline in customer numbers for commercial banks in segments with intense fintech presence (e.g., unsecured consumer loans).
  - Banks sustained overall credit volumes by targeting high-value clientele and increasing average loan amounts per borrower; examples include home equity loans for mortgage clients.
- Risk-taking and asset quality:
  - No evidence of a change in incumbent banks’ risk-taking behavior attributable to fintech competition.
  - NPL ratios for credit card loans rose significantly during 2021-23, largely reflecting fintechs’ rapid expansion into higher-risk, first-time borrowers.
  - Fintech competition produced no discernible effect on commercial banks' NPL ratios or provisioning expense ratios.

### Implications for Welfare and Financial Intermediation
- Lower credit costs improve consumer welfare by reducing borrower expenses.
- Fintech entry plus incumbents’ rate reductions contributed to sustained consumer credit growth since 2022 despite restrictive monetary policy.
- Developments indicate notable improvements in financial intermediation efficiency since the 2018 Financial Sector Assessment by the IMF (FSSA, 2018).

_International Monetary Fund — IMF WORKING PAPERS: Fintech Competition and Banks’ Shrinking Margins in Brazil — INTRODUCTION_

### Section III details the dataset and estimation strategy. Section IV presents the estimated effects of fintech

### Section III–VI: Dataset, Estimation Strategy, and Estimated Effects of Fintech Competition on Commercial Banks

### The Rise of Fintech in Brazil
- Fintech activity spans digital banking, currency exchange, lending, financial management, investments, insurance and payment systems.
- Drivers of fintech expansion: service gaps left by traditional banks, high smart phone usage, and regulatory support.
- Regulatory context and milestones:
  - CMN Resolution 4,480, of April 25, 2016: permitted opening, maintaining, and closing deposit accounts exclusively through digital channels.
  - CMN Resolution 4,656, of April 26, 2018: defined authorization and modalities for credit fintechs, including SCDs (balance-sheet lending) and SEPs (peer-to-peer lending).
  - CMN Resolution 4,658, of April 26, 2018: established requirements for contracting data processing and cloud computing services.
  - CMN Resolution 4,753, of September 26, 2019: enabled simplified digital relationship procedures proportionate to risk and nature of relationship.
  - BCB Resolution 29/2020 and BCB 50/2020: guidelines for a Regulatory Sandbox for testing business models under special temporary authorization.
- Market structure and scale:
  - The analysis includes approximately 195 non-banking credit companies operating in Brazil as of end-2024.
  - Nubank reached 100 million customers in Brazil by end-2024, of which 22 million accessed formal financial services for the first time.
  - The number of active borrowers rose from 75 million in 2018 to 115 million in 2023, with most of the increase attributable to fintech companies.
- Financial stability and policy responses:
  - Household debt-to-income ratio rose from 37% in 2018 to nearly 50% in 2022.
  - Over 45% of consumers defaulted on financial obligations in 2023, compared to less than 40% in January 2018.
  - The Desenrola program (started in July 2013) aided over 15 million people to renegotiate around R$50 billion in overdue debt by May 2024.
  - Capital requirements: effective from January 2023 with progressive implementation until 2025, fintech conglomerates controlled by payment institutions but integrated with financial institutions face the same capital requirement as banks of similar scales.
  - Digital banks comprised only 7% of total funding as of December 2024 (Financial Stability Report, 2025).

### Dataset and Empirical Strategy
- Focus and coverage:
  - Data source: Central Bank’s quarterly reports of financial institutions (IF database).
  - Coverage: all authorized, normally operating financial institutions including about 100 commercial banks, 900 credit unions, and 300 non-banking credit companies and payment institutions.
  - Paper focuses on commercial banks, which account for 85 percent of total credit in the financial system.
  - Period: 2012 to 2024 with quarterly reports.
- Construction and variable definitions:
  - Net interest margin (NIM) = annualized net interest income over total assets.
  - Provision ratio = annualized provision expenses over total loans.
  - Cost of funding ratio = funding expense over total funding.
  - Average lending rates = income from credit operations divided by total loans.
  - For stock variables, a four-quarter moving average is used. Top and bottom 5 percent of observations trimmed for calculated ratios.
- Identification strategy — Bartik exposure:
  - Exposure to fintech competition for bank i at time t constructed as:
    - Exposure_{i,t} = sum over n of (Loan share_{i,n} ⋅ Fintech_share_{n,t})
    - Loan_share_{i,n} is bank i’s share of loan portfolio in credit segment n before significant presence of fintech lenders (pre-existing loan composition calculated using the average share before 2017).
    - Fintech_share_{n,t} is the share of lending by fintech companies in credit segment n at t.
  - Exposure is standardized (number of standard deviations from the mean) for interpretation.
  - Empirical specification:
    - y_{i,t} = α_{i} + β ⋅ Exposure_{i,t} + γ ⋅ X_{i,t} + δ_{year} ⋅ I_{year} + σ_{q} ⋅ I_{q} + ε_{i,t}
    - Outcomes y_{i,t} include average lending rates, funding cost ratio, net interest margin, cost to assets ratio, credit risk (NPL ratios and provisioning ratios), return on equity (ROE), and return on assets (ROA).
  - Controls and inference:
    - Standard bank-specific controls (market share, liquid asset ratio), bank fixed effects, year fixed effects, and quarter fixed effects included.
    - Standard errors clustered at bank level.
- Exogeneity diagnostics:
  - Balance check: tested correlation between exposure and bank characteristics (including share of credit card loans); correlations not significant.
  - Placebo test: no pre-trend in outcome variables before significant fintech competition.
  - Pre-existing loan composition fixed using average before 2017.

### Banks’ Response to Fintech Competition — Main Findings
- Overview:
  - Incumbent banks preserved total loan portfolio size by reducing lending rates and increasing loans per borrower, while maintaining overall risk tolerance.
  - Reduced net interest margins led to decreased profitability despite improvements in operational efficiency.

A. Price Effects
- A one standard deviation increase in fintech competition:
  - leads to a 3.7 percentage point reduction in the average lending rates offered by commercial banks (Table 4A, column 4).
  - compresses commercial banks’ NIM by 1.3 percentage points (Table 4A column 2).
- Contextual benchmarks:
  - Brazil’s average NIM was 4.4 percent in 2021.
  - The 3.7 percentage point reduction accounts for approximately one fifth of the interest rate spread observed between 2022 and 2024.

B. Quantity Effects
- Borrower counts and loan size:
  - A one standard deviation increase in fintech exposure is associated with a 32% reduction in borrowers (Table 4B, column 4).
  - A one standard deviation rise in fintech exposure corresponds to a 32% increase in loans per borrower (Table 4B column 6).
- Aggregate loan volume:
  - Despite the decline in customer numbers, commercial banks maintained overall loan portfolio size by increasing average loan size per customer and targeting high-value clients.
- Scale of fintech lending:
  - Fintech lender borrowers reached 60 million in 2023, representing half of all borrowers in the financial system.

C. Efficiency Effects
- A one standard deviation rise in fintech exposure leads to a 0.5 percentage point decrease in the administrative cost-to-asset ratio (Table 4C, column 2).
- This decrease represents approximately one third of the sample’s median cost-to-asset ratio.
- Structural context:
  - Approximately 24 percent of the interest rate spread is attributable to administrative expenses (BCB Financial Stability Report, 2025).
  - Banks accelerated digitalization, closed some physical branches, and opened digital branches since 2017; open finance initiatives and Pix supported digital transformation.

D. Risk Effects
- Measures and results:
  - Credit risk measured by non-performing loan (NPL) ratios and provisioning ratio.
  - Coefficients on exposure to fintech competition are insignificant for both NPL and provisioning ratios (Table C, column 3-6).
- Interpretation:
  - Fintech entry into unsecured consumer loan markets increased overall borrower risk profile (rising NPL ratios), but commercial banks shifted toward high-value, low-risk borrowers and better terms, leaving aggregate commercial bank credit risk largely stable.

E. Profitability
- A one standard deviation rise in fintech exposure:
  - led to a 3.6 percentage point decrease in return on equity.
  - led to a 0.7 percentage point reduction in return on assets.
- Benchmarks:
  - Median return on equity is 9.1 percent.
  - Median return on assets is 1.1 percent.

F. Macro implications (aggregate effects since 2018)
- From 2018 to 2024 average fintech competition rose by 0.7 standard deviations.
- Applying model coefficients to this change implies:
  - 2.7 percentage point reduction in average lending rates.
  - 0.9 percentage point decrease in net interest margins.
  - 0.4 percentage point decline in administrative cost-to-asset ratio.
  - 0.5 percentage point reduction in return on assets.
- These represent:
  - 15 percent of the interest rate spread (as measured in 2024).
  - 20 percent of net interest margins (as measured in 2024).
  - 12 percent of the administrative cost-to-asset ratio (as measured in 2024).
  - 33 percent of the return on asset ratio (as measured in 2024).
- Macroeconomic context:
  - BCB initiated rate increases in 2022 and tightened policy from September 2024, raising the policy rate to 15 percent by June 2025, with real interest rates nearing 10 percent.
  - Despite tightening, credit growth remained strong, partly driven by fintech lenders’ expansion and competitive effects on banks.

### Robustness Checks and Alternative Estimators
- Alternative estimators considered:
  - Leave-one-out Bartik exposure (excluding each bank’s own credit when calculating the fintech shock).
  - Earlier base year (2016 instead of 2017) for pre-existing loan composition.
  - Two-stage-least-squares (2SLS) using Bartik instruments (each loan segment share multiplying time period separately).
- Results:
  - Effects of fintech competition are robust to these alternative estimators (Table 5A).
- Controls for other structural changes:
  - Controlled for credit union presence and number of Pix users; effects of fintech competition remain significant (Table 5B).
  - Observed additional impacts:
    - Credit unions appear to raise lending rates and net interest margins at commercial banks.
    - Pix user numbers have little impact on bank performance.
- Structural developments accounted for:
  - Expansion of credit unions (majority of expansion prior to 2020; consolidation thereafter).
  - Pix instant payment system launched November 2020: 90 percent of adults use Pix and Pix transactions represent 49 percent of electronic payments in Brazil.

*Italic: Source — IMF Working Paper (Section III–VI of the cited PDF).*

### Conclusions

### Conclusions

### Causal effects of fintech competition on Brazilian commercial banks
- Constructed bank-specific Bartik exposure using detailed credit and balance sheet data of Brazilian financial institutions to identify causal effects.
- Banks maintained their loan portfolios by reducing lending rates.
- Operational efficiency was enhanced.
- Overall profitability of commercial banks declined due to narrower interest margins.

### Quantified aggregate impact on incumbent banks
- Since 2018, greater presence of fintech lenders has resulted in:
  - a decline in banks’ average lending rates by 2.7 percentage points.
  - a reduction in net interest margins by 0.9 percentage points.
- These developments mitigated Brazil’s historically elevated lending rates, an obstacle to economic development.
- Some analysis indicates that aligning Brazil’s lending spread with the global average may lead to a 5 percent increase in national output (e.g. Joaquim and others, 2023).
- Lower rates driven by heightened competition contribute positively to consumer welfare.

### Operational efficiency
- Evidence that commercial banks improved operational efficiency in response to fintech competition.
- The Financial Stability Report (2025) indicates that the operational efficiency of financial institutions has increased since 2022.
- The paper suggests fintech competition may account for a portion of these efficiency gains among commercial banks.

### Fintechs and consumer credit expansion
- Brazil's post-pandemic recovery marked by robust growth in consumer credit; fintech lending identified as a key driver through two channels:
  - Direct lending by fintech companies expanded significantly:
    - fintechs now account for 25% of the credit card market.
    - fintechs account for 12% of the non-payroll personal loan market.
    - the sector has reached 60 million borrowers, many previously lacking access to credit.
  - Increased competition from fintech firms prompted commercial banks to lower lending rates to maintain loan portfolios.
- These dynamics contributed to sustained consumer credit growth despite the current restrictive monetary policy environment.

### Evolving competition and regulatory changes
- Competition from fintech lenders likely to intensify as they expand beyond unsecured consumer loans.
- Payroll-backed loans historically had high entry barriers (partnerships with payroll administrators and accreditation by public bodies).
- In March 2025, the government reformed private-sector payroll-guaranteed credit lines to simplify the process:
  - Private-sector employees can now request loans through a centralized government app.
  - Loans use direct payroll deductions and 10 percent of severance funds as collateral.
  - Bilateral agreement between the employer and a bank is no longer required as was the case previously.
  - This effectively lowered entry barriers for fintech lenders.
- The credit portability program is being further developed by improving information sharing within the framework of Open Finance.

### Policy implications, lessons, and future research
- Brazil’s approach to fintech innovation offers insights for other countries:
  - Implemented proportional regulation through specific licenses and regulatory sandboxes for fintech operations, allowing growth without meeting established banks' requirements.
  - When fintech companies reached scales similar to established banks, the BCB tightened regulations to ensure financial stability and a level playing field.
- Rapid consumer credit expansion has increased household indebtedness, affecting consumer welfare.
- The role of macroprudential measures, stronger consumer protection laws, and better financial education in safeguarding financial stability and improving household financial health could be analyzed in future research.

*IMF Working Paper — Conclusions section*

### References

### References

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### Figures, Captions, and Key Table Extracts
- Figure 1. Brazil: Stylized Facts of Banking Sector Competition
  - A. "Brazil’s lending rates are exceptionally high, with interest spread of more than 30 percent."
  - B. "Administrative costs and profit margins explain nearly half of the interest rate spread in Brazil."
  - C. "Banking sector concentration has also declined since 2018."
  - D. "Markups have also declined since 2020, implying more competition."

- Figure 2. Brazil: The Rise of Fintech Lenders
  - A. "The number of nonbank credit companies, including digital banks, have doubled since 2018"
  - B. "Four of the top ten digital banks worldwide are Brazilian."
  - C. "Fintech lending is concentrated in the unsecured consumer loans market."
  - D. "Access to credit has improved in Brazil, mainly driven by fintech lenders (incl. digital banks)."
  - E. "Fintech lenders' NPL ratios are higher due to the riskier loan portfolio."
  - F. "Nonbank consumer loan credit increased eightfold since 2018"

- Figure 3. Market Share of Fintech Lenders
  - Note: "fintech lenders refer to those classified as category n1 (nonbanking credit companies) in the BCB database."

- Figure 4. Pre-trends for Credit Segments with Strong Fintech Presence
  - Panels: "Panel A: Average Lending Rates", "Panel B: Net Interest Margins", "Panel C: Administrative Cost to Asset Ratio", "Panel D: Return on Assets"
  - Notes: "these figures report pre-trends for the exposure to fintech and the two main credit segments that were exposed to fintech competition as reported in Figure 3. The figures plot the reduced form effect of these credit shares on each of the four outcome variables, with credit shares and controls fixed at the pre-2018 levels. As in the main specification, the bank and time fixed effects are also included."

- Figure 5. Cumulative Effects of Fintech Lending on Commercial Banks
  - A. "Lending Rates and Net Interest Margins"
  - B. "Administrative Costs and Profitability"
  - Note: "the cumulative effects are determined by multiplying the average cumulative change in banks' exposure to fintech competition by the coefficients presented in Table 3."

- Figure 6. Market Share of Credit Union Lenders
  - Note: "Credit unions refer to those classified as category B3S (nonbanking credit companies) in the BCB database."

- Table 1. Implementation of New Rules for Type 3 Conglomerates (excerpt)
  - Columns: "Jan - Dec 2023", "Jan - Dec 2024", "After Jan 2025"
  - Minimum common equity: 4.50% | 4.50% | 4.50%
  - Common equity tier 1: 5.50% | 6.00% | 6.00%
  - Minimum total capital: 6.75% | 7.50% | 8.00%
  - Capital conservation buffer: 0.00% | 1.25% | 2.50%
  - Minimum total capital + capital conservation buffer: 6.75% | 8.75% | 10.50%
  - Source: S&P Global Ratings.

- Table 2. Summary Statistics of Key Variables (excerpt)
  - Exposure to fintech competition: Mean 0.01, Median 0.01, SD 0.01, Min 0.00, Max 0.16
  - Net interest margin: Mean 6.92, Median 5.02, SD 6.74, Min -3.73, Max 34.13
  - Average interest rate: Mean 28.78, Median 25.60, SD 14.14, Min 7.72, Max 68.47
  - Cost of funding ratio: Mean 2.80, Median 2.34, SD 2.83, Min -0.03, Max 82.74
  - ROE: Mean 9.13, Median 10.51, SD 21.12, Min -82.10, Max 54.76
  - ROA: Mean 1.07, Median 1.24, SD 3.54, Min -15.35, Max 16.85
  - Operating cost over total assets: Mean 3.48, Median 2.25, SD 3.27, Min 0.00, Max 14.67
  - NPL: Mean 6.32, Median 3.81, SD 10.53, Min 0.00, Max 100.00
  - Provision ratio: Mean 3.45, Median 2.66, SD 3.62, Min -4.40, Max 12.90
  - Market share: Mean 0.94, Median 0.04, SD 3.39, Min 0.00, Max 22.49
  - Liquid asset ratio: Mean 43.59, Median 40.50, SD 22.36, Min 0.00, Max 226.76
  - Note: "the summary statistics are obtained after data cleaning, including trimming the outliers for accounting ratios."

- Table 3. Balance Check: Relationship Between Credit Shares and Characteristics (selected coefficients)
  - Dependent variables: Credit Card (1), Nonpayroll (2), Working Capital (3), Other NFC Loans (4)
  - Market Share coefficients: 0.002 (1), -0.002 (2), -0.011 (3), -0.004 (4)
    - Standard errors: (0.002), (0.004), (0.008), (0.006)
  - Liquid Asset Ratio coefficients: -0.000 (1), -0.001 (2), 0.003 (3), 0.002 (4)
    - Standard errors: (0.000), (0.001), (0.002), (0.002)
  - Equity Ratio coefficients: -0.000 (1), -0.000 (2), -0.000 (3), -0.001 (4)
    - Standard errors: (0.000), (0.001), (0.001), (0.001)
  - Constants: 0.051*** (1), 0.091*** (2), 0.153*** (3), 0.001 (4)
    - Standard errors: (0.014), (0.027), (0.053), (0.038)
  - Observations: 136 for each column
  - Adjusted R-squared: 0.030 (1), 0.000 (2), 0.054 (3), 0.040 (4)
  - Note: "the credit shares are fixed at the pre-2018 levels, consistent with the Bartik exposure construction."

- Table 4. Effects of Fintech Competition: Baseline Results (selected panels and coefficients)
  - Panel A. Price Effects (columns for Net Interest Margin, Cost of Funding ratio, Average Lending Rates)
    - Exposure to Fintech (normalized) coefficients:
      - Net Interest Margin (columns 1–2): -1.30***, -1.29*** (standard errors (0.48), (0.48))
      - Cost of Funding ratio (columns 3–4): -3.76***, -3.70*** (standard errors (0.95), (0.94))
      - Average Lending Rates (columns 5–6): 0.05, 0.05 (standard errors (0.15), (0.15))
    - Constants (Net Interest Margin): 6.90***, 7.90*** (standard errors (0.02), (0.71))
    - Observations: 2552 (Net Interest Margin), 2311 (Cost of Funding ratio), 2765 (Average Lending Rates)
    - Adjusted R-squared: 0.693, 0.610, 0.649 respectively
    - Fixed effects: Bank, Year, Quarter — Yes for all regressions
    - Note: "Standard errors are clustered at bank level. * p<0.10, ** p<0.05, *** p<0.010."
  - Panel B. Quantity Effects (selected coefficients)
    - Exposure to Fintech (normalized) coefficients (Log(customers), Log(loans per customer), Log(loans)):
      - -0.00, -0.01, -0.31*, -0.32**, 0.32***, 0.32*** (with standard errors (0.12), (0.11), (0.17), (0.16), (0.11), (0.11))
    - Constants reported (examples): 14.37***, 15.22***, 9.08***, 9.69***, 5.31***, 5.57*** (with small standard errors)
    - Observations and Adjusted R-squared show high fit (e.g., Adjusted R-squared up to 0.943).
  - Panel C. Efficiency and Risk Effects (selected coefficients)
    - Exposure to Fintech (normalized) coefficients:
      - Admin cost to assets: -0.50***, -0.51*** (standard errors (0.16), (0.16))
      - NPL ratio: -0.96, -0.92 (standard errors (1.01), (0.96))
      - Provision ratio: -0.08, -0.11 (standard errors (0.34), (0.34))
    - Liquid asset ratio coefficients: 0.01, 0.08**, 0.02** for various specifications (standard errors reported)
    - Observations: 2515, 2772, 2279 depending on outcome
    - Adjusted R-squared: 0.818, 0.464–0.495 range across specifications
  - Panel D. Profitability (selected coefficients)
    - Exposure to Fintech (normalized) coefficients (ROE, ROA):
      - -3.62** and -3.63** for ROE specifications (standard errors (1.59), (1.59))
      - -0.68*** and -0.68*** for ROA specifications (standard errors (0.18), (0.18))
    - Market share, Liquid asset ratio, and Constants reported for each regression
    - Observations: 2613, 2613, 2680, 2680
    - Adjusted R-squared: 0.486, 0.486, 0.474, 0.474

- Table 5. Robustness Checks (selected results)
  - Panel A. Robustness to Alternative Estimators (Net Interest Margin, Average Lending Rates, Admin cost to assets)
    - Exposure to Fintech coefficients (examples across estimators: Baseline, Leave-one-out, Base year, 2SLS)
      - Net Interest Margin: -1.29***, -1.30***, -1.50***, -1.45*** (standard errors (0.48), (0.48), (0.35), (0.55))
      - Average Lending Rates: -3.70***, -3.71***, -3.83***, -4.64*** (standard errors (0.94), (0.94), (0.86), (1.08))
      - Admin cost to assets: -0.53***, -0.52***, -0.55***, -0.71*** (standard errors (0.17), (0.17), (0.17), (0.18))
    - Observations vary by specification: e.g., 2552, 2342, 2311, 2133, 2533, 2331
    - Adjusted R-squared examples: 0.693, 0.610, 0.818
  - Panel B. Robustness to Controlling for Other Structural Changes
    - Additional controls include "Exposure to Credit Union" and "Log (Pix Users)" in alternative specifications
    - Example coefficients:
      - Exposure to Credit Union: 1.45*; 4.90***; 0.34 (standard errors (0.81), (1.48), (0.31))
      - Log (Pix Users): 0.03; 0.07; 0.01 (standard errors (0.04), (0.09), (0.01))
    - Across robustness checks, main Exposure to Fintech coefficients remain statistically significant in many specifications (see Panel A values above).

*Fintech Competition and Banks’ Shrinking Margins — Working Paper No. WP/2026/007*

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_Source: https://www.imf.org/-/media/files/publications/wp/2026/english/wpiea2026007-source-pdf.pdf_