AI in credit decisions is genuinely controversial because it could either democratize lending and expand access to underserved populations or take historical discrimination and amplify it at scale. The reality is both are happening simultaneously in different institutions—it all depends on how intentionally the AI is designed and monitored for fairness.

Sam and Mac examine how AI is disrupting traditional credit scoring. FICO scores have dominated for decades using limited data: payment history, credit utilization, length of credit history, types of credit, and recent inquiries. This approach systematically excludes millions who don't have traditional credit histories, even if they're perfectly responsible with money and would be excellent borrowers.

The technical models include XGBoost as the industry standard and neural networks for processing more data with hidden layers. Traditional logistic regression is often a poor fit for real-world credit behavior. Banks need model governance with clear ownership, regular bias testing, robust explainability, and human oversight for complex cases. AI handles straightforward approvals and denials; humans handle the middle—complex situations requiring judgment and contextual understanding.