In 1958, David Cox published the logistic regression model. That same year, the first FICO score went into production. Sixty‑six years later, a loan officer in Des Moines and a regulator in Frankfurt still trust that same equation more than any neural network. This is not nostalgia. It is a verdict on what lending actually needs.

This post continues the series on why generalized linear models (GLMs) with a logit link remain the workhorse of consumer credit, and where they finally break.

Why This Post?

Credit scoring demands interpretability and stability over raw accuracy. While neural networks can push AUC past 0.92, GLMs with a logit link still power most production systems at top banks. This post explains why, and when to break the rule. GLM is not "less AI". It is a different flavor of AI, one that prioritizes transparency.

For Everyone

The problem

Your credit decision must be explainable. To regulators (e.g. CFPB, ECB), to applicants (adverse action notices), and to your own risk committee.…