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Propensity models are very demanding in banks and every improvement of these models could bring a lot of additional business value. The classification is solved by them and the logistic regression is a frequent statistical method for the model estimation. An application of cluster analysis could discover a hidden data structure in the data set, which could possibly improve the usability of the bank’s propensity models. A special type of cluster analysis is regression clustering, which stands on the model-based approach. It is considered, that each cluster is presented by regression hyperplane (an application of the linear regression is the most frequent in the literature). The objective is to determine the underlying number of clusters in the data set with the simultaneous application of regression functions to each created cluster. The aim of this contribution is to estimate the optimal number of clusters in logistic regression clustering of a data set for a propensity model by using an information-based technique, such as the Bayesian information criterion and the integrated complete-data likelihood criterion. The lowest absolute value of a certain criterion determines the optimal number of clusters. For the model-based clustering application, the R program with the Mclust package is used to fulfill the stated goal.

Application of Logistic Regression Clustering in Bank’s Propensity Models