The Effect of Auditor Attestation and Tolerance for Ambiguity on Commercial Lending Decisions
Bibliographic record
Abstract
We begin this study by developing a model of the decisions made by bank loan officers when they evaluate a commercial loan. The model indicates that loan officers make three sequential decisions: level of risk associated with the loan, whether to recommend the loan, and the interest rate to be charged. We assume that the financial information included with a commercial loan application can be audited, reviewed, or prepared by management with no involvement by their auditors. We argue that the level of attestation should affect the perceived credibility, or conversely, the relative amount of ambiguity of the financial statements presented by management. Tolerance for ambiguity should affect how commercial lending officers handle this ambiguity. We test these effects by varying the level of attestation in a between-subjects experiment with commercial loan officers. Subjects are asked to make judgments on the risk of the loan, whether they would recommend the loan, and the interest rate to be charged. Subjects also completed a tolerance-for-ambiguity instrument. Results of the study indicate that only tolerance for ambiguity significantly affects the risk-assessment judgment. Auditor attestation had no effect on risk assessment. Risk assessment in turn significantly affects the decision to recommend the loan. Finally, the previous risk-assessment decision, tolerance for ambiguity, and the interaction between attestation and tolerance for ambiguity significantly affect the interest rate decision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".