Modeling the contribution of speeding and impaired driving to insurance claim counts and costs when contributing factors are unknown.

Problem: There are no specific indicators for distinguishing insurance claims related to speeding and impaired driving in the information warehouse at the Insurance Corporation of British Columbia. Contributing factors are only recorded for that part of the claim data that is also reported by the police. Most published statistics on crashes that are related to alcohol or speeding are based on police-reported data, but this represents only a fraction of all incidents.
Method: This paper proposes surrogate models to estimate the counts and the average costs associated with speeding and impaired driving to insurance claims when contributing factors are unknown. Using police-reported data, classification rules and logistic regression models are developed to form such estimates. One approach applies classification rules to categorize insurance claims into those related to speeding, impaired driving, and other factors. The counts and the costs of insurance claims for each of these strata and overall are then estimated. A second method models the probability that an insurance claim is related to speeding or impaired driving using logistic regression and uses this to estimate the overall counts and the average costs of the claims. The two methods are compared and evaluated using simulation studies.
Results: The logistic regression model was found to be superior to the classification model for predicting insurance claim counts by category, but less efficient at predicting average claim costs.
Impact: Having estimates of counts and costs of insurance claims related to impaired driving or speeding for all reported crash events provides a more accurate basis for policy-makers to plan changes and benefits of road safety programs.