Affirm launches transformer-based machine learning model for real-time underwriting

Affirm is bringing the same class of AI that powers large language models into consumer lending. The company unveiled a transformer-based machine learning model designed for real-time, transaction-level underwriting, a move that could reshape how the entire buy-now-pay-later industry thinks about credit risk. The model, showcased during Affirm’s Investor Forum, represents roughly 18 months of development work. In experimental testing, it outperformed the company’s existing mainline underwriting system, establishing what Affirm considers a new performance baseline for its credit decisioning engine. What transformers bring to lending Transformer architecture is the backbone of models like ChatGPT and Gemini. Its defining feature is something called an “attention mechanism,” which lets the model weigh different pieces of input data against each other to find patterns that simpler models miss. In Affirm’s case, it means understanding which financial signals matter most for a specific borrower at a specific moment. For Affirm, this translates into better risk assessment and smarter term selection on every individual transaction. Rather than relying on a static credit score that might be...