QED Insight #0002: Data quality is the model.

Teams spend weeks on algorithm choice and hyperparameter tuning. But by the time you fit a model in credit risk, the decisions that matter most are already locked in - and they were made in the data.

How you define default (90 DPD? 120? charge-off? do modifications count?) sets your event rate and every reserve downstream of it. Loans still performing at the end of your observation window aren't non-defaults - they're censored, and treating them otherwise bakes in bias. Pool vintages without a seasoning control and your "default rate" is a maturity artifact, not a measure of risk.

None of this shows up in AUC. You can tune a flawless model on a broken target and every metric will look great - because they're all computed against the same wrong label.

The unglamorous work - defining the event, handling censoring, controlling for vintage - is what decides whether anything you build on top of it is valid. In regulated credit risk, the data definition IS the model. The algorithm is almost an afterthought.