Episode 3 of The Data Science Podcast dives into a real-world case study: how a $12 billion regional bank in the Midwest overhauled its consumer credit scoring model using ensemble methods. Lucas walks through the specific problem — a legacy logistic regression that was rejecting too many thin-file applicants — and how a gradient-boosted tree ensemble, combined with a neural network meta-learner, lifted default prediction accuracy by 18 percent while approving 14 percent more borrowers. Luna presses on the operational trade-offs: model explainability, regulatory compliance under the Equal Credit Opportunity Act, and the compute cost of maintaining two models in production. The episode closes with a data-ethics question: when a better model widens credit access, who decides what 'better' means?

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