UM-Model 1 distinguishes genuine credit risk from demographic bias, detects fraud through causal patterns, and optimizes lending decisions by understanding true financial causality.
Why causal reasoning enables fair lending
Observes: "Applicants from ZIP code 10001 have 30% higher default rate"
Deny loans or charge higher rates for all 10001 applicants ✗
Confounding! High defaults caused by economic downturn in that area, not inherent borrower risk. Creates discriminatory lending.
Builds causal graph: Economic Conditions → Defaults, Payment History → Risk, Location → Economic Conditions
"What would default risk be if economic conditions normalized?" Identifies true borrower risk
Assess based on payment behavior and income stability, not location. Fair lending increases approvals 15% ✓
Explore causal factors in banking operations
Proven results from financial institutions
Separated causal payment behavior from demographic correlations. Increased approvals 15% while reducing defaults 12%
Causal analysis distinguished unusual but legitimate transactions from actual fraud patterns. Saved $4.5M annually
Identified causal dissatisfaction factors vs correlated life events. Targeted interventions reduced churn 24%
Separated market correlation from causal risk factors. Portfolio optimization improved returns 18%
Analyze loan applications with causal modeling
Adjust parameters and click "Run Analysis" to see credit assessment