On the convergence of credit risk in current consumer automobile loans
Bibliographic record
Abstract
Abstract Risk-based pricing within consumer lending is ubiquitous. It considers both prevailing interest rates and the credit profile of a borrower to determine the cost of borrowing. All else equal, higher default risks pay higher borrowing costs. This cost is the annual percentage rate (APR), and it is set at the loan’s origination. A borrower’s credit profile is dynamic, however, and the risk of default gradually declines for current loans. In this article, we derive a novel large-sample statistical hypothesis test suitable for loans sampled from asset-backed securities to populate a credit risk transition matrix between consumer credit risk groups. We find that current loans in all risk groups eventually converge to the top credit tier before scheduled termination, a phenomenon we call credit risk convergence. We then use these convergence estimates for two empirical economic studies. We first estimate that lender conditional risk-adjusted expected profits significantly increase as high-risk, high-APR borrowers stay active and paying. We then estimate current borrowers are entitled to $1,153–2,327 in potential credit-based savings from their improving risk profiles. Because we study consumer auto loans, a large-scale and essential economic good, we opine on the social implications of these results and suggest areas of further study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".