## Dataset overview

## Source details

**Canonical URL:** [Dataset overview](https://www.imf.org/-/media/files/publications/gfsr/2022/april/data/ch3data.xlsx)

## Other formats

- [Markdown version](/-/media/files/publications/gfsr/2022/april/data/ch3data.xlsx.md)
- [Structured JSON version](/-/media/files/publications/gfsr/2022/april/data/ch3data.xlsx.json)

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### Dataset scope and structure
- Worksheet: "GFSR Chapter 3 Apr. 2022" — rowCount 32, columnCount 10; includes publication metadata rows with timestamps "2022-04-07T00:00:00.000Z".
- Worksheet: "Table of Contents" — rowCount 24, columnCount 22; references "'GFSR Chapter 3 Apr. 2022'!B23:J23" with result "Chapter 3. The Rapid Growth of Fintech: Vulnerabilities and Challenges for Financial Stability".
- Multiple figure worksheets with series data and panel layouts (examples below): "Figure 3.1.", "Figure 3.3.", "Figure 3.4.", "Figure 3.5.", "Figure 3.6.", "Figure 3.7.", "Figure 3.8.", "Figure 3.9.", "Figure 3.10.", "Figure 3.11.", "Figure 3.12.".

### Key figures and labeled series (selected samples)
- Figure 3.1. The Rise of Fintech Firms and Decentralized Finance
  - Panel: "Growth of Assets of Fintech Lenders (2013:H1=100)"
  - 2013H1: Traditional bank 100; FinTech bank 99.99999999999999; Traditional nonbank 100; FinTech nonbank 100
  - H2: Traditional bank 101.44627436645307; FinTech bank 101.05700100896854; Traditional nonbank 102.95056223880653; FinTech nonbank 109.86935499960242
  - 2014H1: Traditional bank 103.63679678301055; FinTech bank 103.78904095035134; Traditional nonbank 104.31892647506832; FinTech nonbank 108.70904255755873
- Figure 3.4. Client Profile of Neobanks — 1. Brazilian Banks: Customer Breakdown (Percent, share of loans)
  - Middle- and higher-income: Neobank 0.43999999999999995; Traditional peers 0.6499999999999999
  - Lower-income: Neobank 0.56; Traditional peers 0.35000000000000003
- Figure 3.7. Fintechs in the US Home Mortgage Market
  - Panel 1. Annual US Home Mortgage Originations (tr. USD, left scale; rank, right scale)
    - 2007: Non-banks - total 427.3432922363281; Banks 1421.6846923828125; Credit Unions 53.46724319458008; Non-banks - non-FinTech 396.4828796386719; FinTechs 30.860403060913086; Rank of Rocket Mortgages (right side) 17
    - 2008: Non-banks - total 292.7367248535156; Banks 949.5552978515625; Credit Unions 64.65534973144531; Non-banks - non-FinTech 270.1350402832031; FinTechs 22.601696014404297; Rank of Rocket Mortgages (right side) 16
    - 2009: Non-banks - total 440.083740234375; Banks 1218.213623046875; Credit Unions 86.65132904052734; Non-banks - non-FinTech 396.16900634765625; FinTechs 43.91476058959961; Rank of Rocket Mortgages (right side) 10
    - 2010: Non-banks - total 420.551513671875; Banks 1073.154541015625; Credit Unions 74.82835388183594; Non-banks - non-FinTech 366.2111511230469; FinTechs 54.34040832519531; Rank of Rocket Mortgages (right side) 6
    - 2011: Non-banks - total 378.1653747558594; Banks 951.873291015625; Credit Unions 62.04268264770508; Non-banks - non-FinTech 325.5615539550781; FinTechs 52.60380172729492; Rank of Rocket Mortgages (right side) 4
  - Panel 3. Age Distribution of Mortgage Borrowers — Banks: <25 2.5465046347577016; 25-34 25.343427205694212; 35-44 29.783019689564938; 45-54 19.629626758487415; 55-64 12.358159495017803; 65-74 5.5293889047803715; >74 1.3315872371146056
- Figure 3.8. Recent Development of DeFi Lending — 4. Composition of Borrowing and Collateral (Percent)
  - Collateral: Stablecoins 25.368173172041146; Volatile assets 74.63182682795885
  - Borrowing: Stablecoins 90.30459782752726; Volatile assets 9.695402172472734
- Figure 3.9. Decentralized Finance Market Risks — 2. Liquidation Probability and Expected Losses (Percent)
  - All: Probability of liquidation 24.7408975461538; Expected loss from liquidation 0.893497052254866
  - Healthy accounts: Probability of liquidation 2.11550099901446; Expected loss from liquidation 0.133318767914488
  - Unhealthy accounts: Probability of liquidation 30.6163775833991; Expected loss from liquidation 1.09090408223039
- Figure 3.10. Decentralized Finance Liquidity Risks
  - Panel 1. Distribution of the Utilization Rate across Assets and Platforms (Median and maximum value)
    - 2020-01-01T00:00:00.000Z: Other crypto assets Median 7.686797507405819; Max 99.8944322105401; Stablecoins Median 78.1864477144303; Max 85.113044904164
    - 2020-01-02T00:00:00.000Z: Other crypto assets Median 7.665223977399714; Max 77.5451599715023; Stablecoins Median 77.83756564701785; Max 86.5668055681318
  - Panel 2. Liquidity Concentration: Number of Accounts Providing 50 Percent of Liquidity (Distribution by assets, median and 5th to 95th range)
    - Jan.19: 5th percentile 6; Median 6; 95th percentile 6 for stablecoins and other crypto assets; examples include 5.05, 5.5, 5.95 in other sample rows
- Figure 3.11. Cyberattacks on Decentralized Finance
  - 1. DeFi-Related Cyberattacks (Billions of US dollars) and 2. Cumulative Abnormal Returns after Attacks (Percent)
    - 2020Q1: Number of incidents 3; Gross value stolen 11754000; Days Interquartile range 1; Median -19.494814095730785; upperquartile range -2.3362992543282
    - 2020Q2: Number of incidents 2; Gross value stolen 25500000; Days Interquartile range 3; Median -75.28910214819197; upperquartile range -5.430968027749438
    - 2020Q3: Number of incidents 4; Gross value stolen 23621260; Days Interquartile range 4; Median -78.96179456272206; upperquartile range -5.339573105806872
    - 2020Q4: Number of incidents 9; Gross value stolen 88942000; Days Interquartile range 5; Median -78.5375943695335; upperquartile range -8.759809267966457
- Figure 3.12. Efficiency and Risks of Decentralized Finance — 1. Estimated Marginal Costs and Margins (Percent)
  - DeFi Platforms: Average 2.1363007836043835; Price 1.8983726855367422; Marginal Cost 0.23792809806764126; Margin 0; Labor cost 1.8983726855367422; Funding cost 0; Operational cost 0; Other Cost 0
  - Bank (AE): Average 5.575673820803295; Price 3.938696233908067; Marginal Cost 1.6369775868952279; Margin 1.6837377902314132; Labor cost 1.5634408024815798; Funding cost 0.33681100304286354; Operational cost 0.35470663815220993
  - Nonbank (AE): Average 8.18286869674921; Price 5.687156915664673; Marginal Cost 2.4957117810845375; Margin 2.8230202282346575; Labor cost 1.6279589587830563; Funding cost 0.5442437889852108; Operational cost 0.6919339396617481
  - Bank (EM): Average 10.46390176936984; Price 7.160819007083774; Marginal Cost 3.303082762286067; Margin 2.9716318853609964; Labor cost 3.0167423780848766; Funding cost 0.8384463162987619; Operational cost 0.33399842733913854

### Data characteristics and usage notes (from worksheet samples)
- Time-stamped series use ISO 8601 timestamps such as "2020-01-01T00:00:00.000Z" and "2022-04-07T00:00:00.000Z".
- Panel labels preserve units and scales (examples: "(2013:H1=100)", "(tr. USD, left scale; rank, right scale)", "(Percent)", "(Billions of US dollars)").
- Numeric precision is high in many series (multiple values include long decimal representations such as 0.43999999999999995, 101.44627436645307, 74.63182682795885).

*Source: ch3data (GFSR Chapter 3 Apr. 2022 dataset).*

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_Source: https://www.imf.org/-/media/files/publications/gfsr/2022/april/data/ch3data.xlsx_