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A methodology for training, comparing, and selecting scoring models in credit risk is presented. The process uses AI tools like Codex to generate Python scripts, estimate logistic regressions, compute AUC and Gini, and produce summary tables. The final model must be statistically sound, stable, interpretable, and consistent with business expectations.
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