Credit scoring using global and local statistical models

This paper compares global and local statistical models that are used for the analysis of a complex data set of credit risks. The global model for discriminating clients with good or bad credit status depending on various customer attributes is based on logistic regression. In the local model, unsupervised learning algorithms are used to identify clusters of customers with homogeneous behavior. Afterwards, a model for credit scoring can be applied separately in the identified clusters. Both methods are evaluated with respect to practical constraints and asymmetric cost functions. It can be shown that local models are of higher discriminatory power which leads to more transparent and convincing decision rules for credit assessment.