Modeling exposure to losses on automobile leases

We present an integrated statistical model for assessing risk and projecting financial losses on automobile leases. The model employs nonstationary Markovian state transitions for active leases and hierarchical logistic and regression equations for different outcomes on termination. The model reveals that lower residual risks may partially offset higher credit risk for customers whose credit scores predict higher risk of default. It also reveals a risk profile that differs through time from other secured credits such as mortgages. A three-year follow-up of forecasts versus outcomes for 39,500 leasing contracts shows that the model predicted rates of repossession better than standard roll-rate models with stationary transition probabilities. It displayed similar accuracy in predicting unscheduled terminations and insurance settlements.