Five Building Blocks for Compliant AI-driven Lending
The ability to get models through compliance and into production is an increasingly sought-after talent as more lenders recognize ML’s superior results, faster approvals, and greater inclusion. ML is better at classifying risk than traditional credit scores because it can extract more insights from the nuances and patterns in any sizable applicant pool. A good ML model trained on loans from your market area (funded and unfunded) will help you spot many good borrowers overlooked by legacy credit scores.
Zest AI has helped more lenders put AI-driven credit underwriting models into production than anyone on the planet. We’ve learned — sometimes the hard way -- what it takes to get lenders up and running with compliant machine-learning (ML) credit models that approve more good borrowers faster and with less financial and compliance risk than traditional underwriting methods. Download our latest guide to read about:
- Explainability: Understanding how AI works
- Bias and Fair Lending: How to be more inclusive
- Using Data: Keeping it simple for success
- Regulatory Documentation: Updating for AI
- Model Validation: Quality control and monitoring