Summary of Fighting Sampling Bias: a Framework For Training and Evaluating Credit Scoring Models, by Nikita Kozodoi et al.
Fighting Sampling Bias: A Framework for Training and Evaluating Credit Scoring Models
by Nikita Kozodoi, Stefan Lessmann, Morteza Alamgir, Luis Moreira-Matias, Konstantinos Papakonstantinou
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes novel methods for training and evaluating scoring models used in financial institutions. The current approach relies on data from previously accepted applicants with known repayment behavior, which introduces sampling bias. This bias affects model performance and accuracy. To mitigate this issue, the authors suggest two frameworks: bias-aware self-learning, which infers labels for rejected applications to augment biased training data; and a Bayesian framework that extends standard evaluation metrics to account for biased data. The proposed methods demonstrate superior predictive performance and profitability in extensive experiments on synthetic and real-world datasets. Additionally, sensitivity analysis highlights boundary conditions affecting the performance of the novel methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve scoring models used by financial institutions. Right now, these models are trained and tested using data from people who have already been approved for loans. This makes the training data biased because it only includes information about borrowers who have a good track record of repaying their debts. The authors suggest two new methods to address this issue: one that helps the model learn from rejected loan applications, and another that provides a more accurate picture of how well the model will perform in real-world situations. By using these new approaches, the authors found that they can improve the accuracy and profitability of the scoring models. |
