Robust contextual bandit method for optimal loan offering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a Group-DRO enhanced doubly-robust contextual bandit approach to designing optimal policies for loan product offerings.This approach is particularly suited to high-stakes decision-making such as lending decisions, where one must leverage historical data (with inherent biases and uncertainties) to design future policies.By using doubly-robust estimation, we make efficient use of the data and mitigate bias from unknown logging propensities.By incorporating distributional robustness with group-based ambiguity sets, we ensure that the learned policy is insulated against worst-case shifts in each subgroup, thereby protecting the overall performance from crashing if, say, economic conditions change that strongly impact a minority group.By adding fairness constraints such as demographic parity or equal opportunity, we can align the policy with ethical and regulatory standards, ensuring that no group is left behind or unfairly treated by the automated decision process.We present empirical evidence on a small business credit card portfolio, demonstrating significant improvements over standard methods.This proposed framework contributes a step toward responsible AI in finance.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it