Estimation of default probabilities using incomplete contracts data

This paper develops a count data model for credit scoring which allows the estimation of default probabilities using incomplete contracts data. The main advantage of the proposed approach is that it permits a more efficient use of the data, including that for the most recent clients. Moreover, because the probability of default is specified as a function of the age of the contract, the model provides some information on the timing of the defaults. The model is based on the beta-binomial distribution, which is found to be particularly adequate for this purpose. A well-known dataset on personal loans is used to illustrate the application of the proposed model.