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Credit Scores vs Bank Data: Why Lenders Are Switching

In this episode of FinTech Confidential, host Ted Huff talks with Jamie Twist and Casey Kaplan of Carrington Labs about why lenders are moving away from traditional credit scores toward bank transaction data and AI-driven cash‑flow underwriting. They explain how credit scores, unchanged for three decades, often fail to predict risk for thin‑file or gig‑economy borrowers, while real‑time banking data reveals richer behavioral signals that improve default predictions and enable more inclusive lending. Carrington Labs’ platform builds personalized risk scores using machine‑learning models that analyze spending patterns, cash‑flow timing, and other transaction‑level behaviors, allowing lenders to approve more borrowers profitably. The guests stress that this shift not only expands credit access for underserved populations but also reduces risk for lenders by leveraging modern data and AI.