{
  "id": "AIBD-0176",
  "title": "LendingClub's alternative-data grades move some subprime borrowers into lower-priced credit",
  "summary": "Jagtiani and Lemieux found that LendingClub rating grades' correlation with FICO fell from about 80% (2007 loans) to about 35% (2014–15) and that some traditionally subprime borrowers received bett...",
  "date_observed": "2019-11-06",
  "date_basis": "unassessed",
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  "account": "Federal Reserve Bank of Philadelphia working paper 18–15 is by Julapa Jagtiani (Philadelphia Fed) and Catharine Lemieux (Chicago Fed), revised January 2019. Its title is “The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform”. It compares account-level LendingClub loans with similar loans in Y-14M reports from bank holding companies with assets of $50 billion or more. The authors report that the correlation between LendingClub's rating grades and FICO scores declined. It was about 80 percent for 2007 originations and about 35 percent for 2014–2015 vintages. The grades perform well in predicting loan performance over the two years after origination. Alternative data allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into better grades. They got lower-priced credit. For the same default risk consumers pay smaller spreads at LendingClub than on credit cards. For 2015 loans about 8 percent of A-rated and 28 percent of B-rated borrowers had FICO scores below 680. Crossref lists a journal version in Financial Management (DOI 10.1111/fima.12295) issued 6 November 2019. The working paper itself carries only a month.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Assign rating grades and price consumer loans on the LendingClub marketplace.",
  "system": "LendingClub rating-grade model (grades A to G)",
  "model_version": null,
  "developer": "LendingClub",
  "deployer": "LendingClub",
  "location": null,
  "domain": "Consumer credit",
  "segment": "Marketplace consumer lending",
  "benefit_type": "Increased Inclusion",
  "taxonomy_note": "The good is better-priced credit for borrowers traditional criteria would call subprime, and the lender's technique is not documented.",
  "registry_ids": {},
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      "depth": null,
      "reason": "Effects on borrowers are reported as vintage-level shares and spreads, not as outcomes for identifiable individuals."
    },
    {
      "group": "societal",
      "depth": null,
      "reason": "Financial-inclusion effects are discussed at the aggregate level only."
    }
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    "status": "unassessed",
    "statement": "The paper attributes the regrading of subprime borrowers to alternative data used by the lender. It does not establish that machine learning produced the grades. LendingClub developed and deployed its own model. The Federal Reserve authors are independent."
  },
  "evidence": [
    {
      "kind": "preprint",
      "citation": "Julapa Jagtiani and Catharine Lemieux, Federal Reserve Bank of Philadelphia Working Paper 18-15, revised January 2019 (later in Financial Management, DOI 10.1111/fima.12295, issued 2019-11-06 per Crossref)",
      "title": "The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform",
      "url": "https://doi.org/10.21799/frbp.wp.2018.15",
      "date": null,
      "written_by": "independent",
      "quote": "allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into \"better\" loan grades"
    }
  ],
  "replication": {
    "produced_by": [
      "Account-level LendingClub data compared with Y-14M bank data for similar loans, 2007 to 2015 vintages",
      "Lender grading that increasingly used information not captured by FICO"
    ],
    "must_hold": [
      "Alternative data that predicts repayment beyond FICO",
      "Grades that keep predicting loan performance (the paper reports good prediction over two years after origination)"
    ],
    "echoes": [
      {
        "date": null,
        "setting": "NBER working paper 29840, “Invisible Primes: Fintech Lending with Alternative Data” (2022–03)",
        "model_version": null,
        "outcome": "unknown",
        "source": "Crossref listing only"
      }
    ]
  },
  "paired_incident": null,
  "headline": "Some subprime borrowers get lower-priced credit at LendingClub",
  "metric": "about 8 percent of A-rated and 28 percent of B-rated borrowers had FICO scores below 680",
  "comparator": {
    "against": "classification of borrowers by traditional credit criteria",
    "before": "classified as subprime by traditional criteria",
    "after": "slotted into “better” loan grades, which allowed them to get lower-priced credit",
    "basis": "measured",
    "quote": "The use of alternative data has allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into “better” loan grades, which allowed them to get lower-priced credit. In addition, for the same risk of default, consumers pay smaller spreads on loans from LendingClub than from credit card borrowing.",
    "url": "https://www.philadelphiafed.org/-/media/frbp/assets/working-papers/2018/wp18-15r.pdf"
  },
  "later": {
    "status": "unknown",
    "date": null,
    "note": "No later finding on LendingClub's grades was found; the 2022 'Invisible Primes' paper concerns a different platform.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": "United States",
  "deployer_country": "United States",
  "unrated_because": null,
  "brief_notes": null,
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      "change": "Title reworded to state the event without naming who claims it.",
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      "change": "Benefit type set to Increased Inclusion under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.",
      "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
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      "source": "https://www.philadelphiafed.org/-/media/frbp/assets/working-papers/2018/wp18-15r.pdf",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://en.wikipedia.org/wiki/LendingClub; https://en.wikipedia.org/wiki/LendingClub",
      "editor": "AIBD"
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      "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
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          "answer": "about 8 percent of A-rated and 28 percent of B-rated borrowers had FICO scores below 680",
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            "account": "Federal Reserve Bank of Philadelphia working paper 18–15 is by Julapa Jagtiani (Philadelphia Fed) and Catharine Lemieux (Chicago Fed), revised January 2019. Its title is “The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform”. It compares account-level LendingClub loans with similar loans in Y-14M reports from bank holding companies with assets of $50 billion or more. The authors report that the correlation between LendingClub's rating grades and FICO scores declined. It was about 80 percent for 2007 originations and about 35 percent for 2014–2015 vintages. The grades perform well in predicting loan performance over the two years after origination. Alternative data allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into better grades. They got lower-priced credit. For the same default risk consumers pay smaller spreads at LendingClub than on credit cards. For 2015 loans about 8 percent of A-rated and 28 percent of B-rated borrowers had FICO scores below 680. Crossref lists a journal version in Financial Management (DOI 10.1111/fima.12295) issued 6 November 2019. The working paper itself carries only a month.",
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