UM-Model 1 distinguishes true risk factors from confounding variables, enabling accurate pricing, fraud detection, and claims prediction through causal reasoning.
Why causal reasoning prevents pricing errors
Observes: "Customers in ZIP code 90210 have 40% higher claims"
Increase premiums for all 90210 residents by 40% ✗
Confounding! High claims are caused by expensive vehicle repairs in that area, not inherent risk. Unfairly penalizes safe drivers.
Builds causal graph: Repair Costs → Claims, Driving Behavior → Accidents, Location → Repair Costs
"What would claims be if repair costs were normalized?" Identifies true risk factors
Price based on driving behavior, not location. Fair pricing increases retention 23% ✓
Explore causal factors in insurance risk modeling
Proven results from insurance operations
Separated causal risk factors from demographic correlations. Fair pricing increased retention 23%
Causal analysis distinguished legitimate unusual claims from actual fraud patterns. Saved $3.2M annually
Identified preventive care as causal factor reducing future claims. Adjusted incentives saved $1,800 per member
Separated building age correlation from actual structural risk factors. Improved underwriting accuracy
Analyze insurance risk with causal modeling
Adjust parameters and click "Run Analysis" to see risk assessment