Harnessing Soft Information to Promote Financial Inclusion: The Case of Business Lending by a Native CDFI
Bibliographic record
Abstract
Abstract Native Community Development Financial Institutions (NCDFIs) promote financial inclusion in financially underserved Native communities by adopting innovative lending strategies, including designing their own soft-information-based measures of borrower risk. Drawing on business loan data from one prominent NCDFI, a nonprofit loan fund, we examine to what extent the NCDFI-generated borrower risk measures help explain the NCDFI's loan performance and pricing above and beyond the effect of the credit score, a conventional credit-bureau-produced, hard-information-based metric. All else equal, both loan delinquency hazard and loan interest rate are robustly predicted by one of the NCDFI's two proprietary soft-information-based measures, the character score, but do not vary with the other one, commitment to business score. The credit score is an important determinant of loan delinquency hazard but, all else equal, does not exhibit a detectable relationship with the loan interest rate. We do not find evidence of noteworthy interactions among the three NCDFI-used borrower risk measures. Our study offers evidence in support of the unique underwriting practices and relationship-based lending operations that characterize the NCDFI industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".