Research Paper on Conditional Weight of Evidence for Gradient Boosting Credit Models Released
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Agus Sudjianto and Denis Burakov have released a research paper on conditional weight of evidence for gradient boosting credit models, proposing a conditional weight of evidence method to measure the marginal impact of a single credit feature on the log-odds of default, given the rest of an applicant's credit information. A distinctive feature of this method is that the same feature value can yield different evaluations for applicants with different credit backgrounds. The paper was published on arXiv's q-fin (Quantitative Finance New Papers) section on October 20, 2026.
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- arXiv q-finConditional Weight of Evidence for Gradient Boosting Credit Models: Estimation Methods, Additivity, and Their Relationship with Adverse Behavior Causes
This arXiv paper proposes a conditional weight of evidence method to measure the marginal impact of a single credit feature on the log odds of default, given the rest of an applicant's credit information. The same feature value can yield different evaluations for applicants with different credit backgrounds.
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