Cluster-based dynamic scoring model

Importance of early prediction of bad creditors has been increasing extensively. In this paper, we propose a behavioral scoring model which dynamically accommodates the changes of borrowers’ characteristics after the loans are made. To increase the prediction efficiency, the data set is segmented into several clusters and the observation period is fractionized. The computational results showed that the proposed model can replace the currently used static model to minimize the loss due to bad creditors. The results of this study will help the loan lenders to protect themselves from the potential borrowers with high default risks in a timely manner.