This paper presents a model for the determination and forecast of the number of defaults and credit changes by estimating a reduced-form ordered regression model with a large data set from a credit insurance portfolio. Similarly to banks with their classical credit risk management techniques, credit insurers measure the credit quality of buyers with rating transition matrices depending on the economical environment. Our approach consists in modeling stochastic transition matrices for homogeneous groups of firms depending on macroeconomic risk factors. One of the main features of this business is the close monitoring of covered firms and the insurer’s ability to cancel or reduce guarantees when the risk changes. As our primary goal is a risk management analysis, we try to account for this leeway and study how this helps mitigate risks in case of shocks. This specification is particularly useful as an input for the Own Risk Solvency Assessment (ORSA) since it illustrates the kind of management actions that can be implemented by an insurer when the credit environment is stressed.