Cluster-based dynamic scoring model

Abstract 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.

[1]  L. Thomas.  A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers , 2000 .

[2]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[3]  So Young Sohn,et al.  Managing loan customers using misclassification patterns of credit scoring model , 2004, Expert Syst. Appl..

[4]  John Mingers,et al.  Neural Networks, Decision Tree Induction and Discriminant Analysis: an Empirical Comparison , 1994 .

[5]  William T. Scherer,et al.  Time will tell: behavioural scoring and the dynamics of consumer credit assessment , 2001 .

[6]  Vijay S. Desai,et al.  A comparison of neural networks and linear scoring models in the credit union environment , 1996 .

[7]  Rashmi Malhotra,et al.  Differentiating between Good Credits and Bad Credits Using Neuro-Fuzzy Systems , 2001, Eur. J. Oper. Res..

[8]  David J. Hand,et al.  Optimal bipartite scorecards , 2005, Expert Syst. Appl..

[9]  David E. Booth,et al.  A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms , 2005, Expert Syst. Appl..

[10]  Yiu-ming Cheung,et al.  k*-Means: A new generalized k-means clustering algorithm , 2003, Pattern Recognit. Lett..

[11]  H. McNab,et al.  Principles and practice of consumer credit risk management , 2003 .

[12]  Tom Heskes,et al.  Clustering ensembles of neural network models , 2003, Neural Networks.

[13]  謝楠楨.  An integrated data mining and behavioral scoring model for analyzing bank customers , 2004 .

[14]  Mu-Chen Chen,et al.  Credit scoring and rejected instances reassigning through evolutionary computation techniques , 2003, Expert Syst. Appl..

[15]  D. Hand.  Modelling consumer credit risk , 2001 .

[16]  Sanghoon Kim,et al.  Improved technology scoring model for credit guarantee fund , 2005, Expert Syst. Appl..

[17]  L. Thomas,et al.  Does Scoring a Subpopulation Make a Difference , 1996 .

[18]  Eric R. Ziegel,et al.  Mastering Data Mining , 2001, Technometrics.

[19]  So Young Sohn,et al.  Reject inference in credit operations based on survival analysis , 2006, Expert Syst. Appl..