Credit & finance data science that holds up in practice.
Why data science behaves differently in credit underwriting than in ordinary ML. Selection bias, causal inference, calibration, validation, fairness, and regulation, in the language of practice.
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[Basics] Part 8. You must be able to say why it decided: interpretability, fairness, and regulation
In credit, a model has to be able to explain why it decided the way it did, and to answer whether that decision is fair. Transparent models and post-hoc explanations, proxy discrimination that seeps back in even after you drop protected attributes, fairness that has no single definition, and the regulation underpinning it all — in the language of practice.
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[Basics] Part 7. Deployment isn't the finish line: validation, governance, and monitoring
In finance, a model isn't finished when you finish building it. Someone other than the builder validates it independently, you switch it on carefully in shadow mode, and after deployment you keep watching — and retrain it once it ages. Here are validation, governance, and monitoring for credit models, in the language of practice.
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[Basics] Part 6. Beyond prediction, to intervention: causal inference and experiments
Credit underwriting doesn't just predict outcomes, it intervenes in them. Ask a question like 'does raising a limit increase defaults?' with correlation and you get the sign wrong before you even start. Here is credit's causal inference put in practitioner terms, from experiments and quasi-experiments to regression discontinuity and uplift.
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[Basics] Part 5. Ranking isn't enough: three axes for evaluating a credit model
How do you know whether you built a good model? In credit you don't just check whether it ranks well (discrimination). You read discrimination with AUC and PR-AUC, check whether the probabilities match reality with calibration, and check whether it holds up over time with PSI. Here are the two axes ordinary ML tends to skip.
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[Review] Can Google's new tabular foundation model TabFM beat GBM in credit? I tested it on public data
Google's zero-shot tabular foundation model TabFM claims to beat even a well-tuned GBM with no training and no tuning. Can it actually be used on credit losses? A practitioner's review, pitting it against a carefully built GBM on public credit-card data.
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[Basics] Part 4. Building a credit model: scorecards and trees
If Part 3 was about choosing a model, this piece is about actually building one. How to build a scorecard with logistic regression (WOE, IV, score scaling) and how to build one with trees (features, SHAP, monotone constraints), where the two diverge, and the reject inference and calibration you have to run no matter which model you picked.
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