Balancing Fairness and Accuracy in Machine Learning-Based Probability of Default Modeling via Threshold Optimization
Bibliographic record
Abstract
This study presents a fairness-aware framework for modeling the Probability of Default (PD) in individual credit scoring, explicitly addressing the trade-off between predictive accuracy and fairness. As machine learning (ML) models become increasingly prevalent in financial decision-making, concerns around bias and transparency have grown, particularly when improvements in fairness are achieved at the expense of predictive performance. To mitigate these issues, we propose a model-agnostic, post-processing threshold optimization framework that adjusts classification cut-offs using a tunable parameter, enabling institutions to balance fairness and performance objectives. This approach does not require model retraining and supports a scalarized optimization of fairness–performance trade-offs. We conduct extensive experiments with logistic regression, random forests, and XGBoost, evaluating predictive accuracy using Balanced Accuracy alongside fairness metrics such as Statistical Parity Difference and Equal Opportunity Difference. Results demonstrate that the proposed framework can substantially improve fairness outcomes with minimal impact on predictive reliability. In addition, we analyze model-specific trade-off behaviors and introduce diagnostic tools, including quadrant-based and ratio-based analyses, to guide threshold selection under varying institutional priorities. Overall, the framework offers a scalable, interpretable, and regulation-aligned solution for deploying responsible credit risk models, contributing to the broader goal of ethical and equitable financial decision-making.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".