The Implications of Credit Risk Modeling for Banks’ Loan Loss Provisions and Loan-Origination Procyclicality
Why this work is in the frame
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Bibliographic record
Abstract
Economic policymakers express concern that procyclical lending by banks imperils financial stability. Prior research finds that banks that record timelier loan loss provisions originate more loans during downturns, consistent with loan-loss-provision timeliness mitigating loan-origination procyclicality. Motivated by this concern and research, we examine whether banks’ credit risk modeling disciplines both their loan loss provisions and loan origination. We identify two forms of credit risk modeling from banks’ financial report disclosures: statistical modeling of the drivers of past loan losses and stress testing of future loan losses to adverse scenarios. We show that banks’ credit-risk-modeling disclosures are positively associated with their loan-loss-provision timeliness, with the ability of their provisions to predict future loan charge-offs, and with their loan origination during downturns. We further show that these associations vary in predictable ways across the two forms of credit risk modeling when we distinguish homogeneous from heterogeneous loans and stable periods from downturns.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it