This is like giving a bank a super-smart calculator that has studied millions of past loans so it can help decide, in a split second, which new customers are safe to lend money to and on what terms.
Traditional credit underwriting relies on blunt rules (credit scores, income thresholds) that miss many good borrowers and sometimes misprice risk. Pagaya’s AI underwriting helps lenders better separate good from risky borrowers, expand approval rates, and price credit more accurately across large volumes of applications.
Access to large-scale loan performance and behavioral data, underwriting models tuned over many credit cycles, and tight integration into lender workflows/decision engines create switching costs and performance advantages that are hard for new entrants to quickly replicate.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Model performance and fairness drift as macroeconomic conditions and borrower behavior change, requiring constant re-training, feature maintenance, and rigorous monitoring to stay accurate and compliant.
Early Majority
Focus on being an AI underwriting infrastructure provider for banks and credit issuers (rather than purely a consumer-facing lender), enabling partners to plug into Pagaya’s models and funding channels while retaining the end-customer relationship.