SupplyLogic

Credit underwriting has historically relied on rule-based decisioning frameworks that apply predetermined thresholds to borrower attributes. A FICO score above 680 combined with debt-to-income below 40% triggers approval, while applications falling outside these parameters receive denials or require manual review. This approach provided transparency and regulatory comfort for decades, but it struggles to capture the nuanced risk patterns that determine actual repayment behavior. As consumer lending portfolios face rising charge-off rates and compressed margins, lenders must choose between refining traditional rule-based systems or adopting machine learning models that promise superior predictive accuracy at the cost of reduced interpretability. The debate over AI in Credit Management often oversimplifies the choice as tradition versus innovation. In reality, both rule-based and machine learning approaches have distinct strengths and weaknesses across different credit lifecycle stages....