Personal Credit Evaluation System Based on Decision Tree

Abstract:

This paper analyzed personal credit evaluation system in rural area based on decision tree model. Firstly, it reviewed some references about credit evaluation methods and found that decision tree was a linear adaptive data-driven model with induction ability and a wide range of function approximation ability so that it could be applied into personal credit evaluation. Secondly, decision tree classified data samples consisting of two phases: constructing decision tree model and then classification stage. The first stage was to train data samples to establish a decision tree, and this process was divided into three steps which included feature selection, node splitting and tree pruning. The second stage was to put test samples into the established decision tree, and let it to classify from a new set of data. After that, it took advantage of the model to evaluate personal credit and selected the twenty indicators. The results showed that household assets, net assets and the existing current account balance were the most important three indicators for evaluating personal credit in rural area.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 5840-5843
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.5840
• Online since: September 2014
• Authors: Li Na Tan, Hai Long Wu*
• Keywords: Credit Evaluation, Decision Tree, Personal Credit

References

[1] Odom M D, Sharda R . A neural network model for bankruptcy prediction . Proceedings of the IEEE International Joint Conference on Neural Networks: 1990(2): 163-168.

DOI: 10.1109/ijcnn.1990.137710

[2] Tam K Y, Kiang M Y. Managerial applications of neural networks: the case of bank failure predictions. Management science, 1992, 38(7): 926-947.

DOI: 10.1287/mnsc.38.7.926

[3] Coats P K, Fant L F. Recognizing financial distress patterns using a neural network tool. Financial Management, 1993: 142-155.

DOI: 10.2307/3665934

[4] Bell T. Neural nets or the logic model: a comparison of each models ability to predict commercial bank failures. Intelligent Systems in Accounting, Finance and Management: 1997(63): 249-264.

DOI: 10.1002/(sici)1099-1174(199709)6:3<249::aid-isaf125>3.0.co;2-h

[5] Manzoni K. Modeling Eurobond credit ratings and forecasting downgrade probability. International Review of Financial Analysis, 2004, 13(3): 277-300.

DOI: 10.1016/j.irfa.2004.02.010

[6] Frydman H, Altman E I, KAO D L I. Introducing recursive partitioning for financial classification: the case of financial distress. The Journal of Finance, 1985, 40(1): 269-291.

DOI: 10.1111/j.1540-6261.1985.tb04949.x

[7] Martinelli E, Carvlho A D, Rezende S, Matias A. Rules extractions from banks' bankrupt data using connectionist and symbolic learning algorithms. In Proc. Computational Finance. New York: (1999).

[8] Nie G, Rowe W, Zhang L, et al. Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 2011, 38(12): 15273-15285.

DOI: 10.1016/j.eswa.2011.06.028

[9] Zhao H. A multi-objective genetic programming approach to developing Pareto optimal decision trees. Decision Support Systems, 2007, 43(3): 809-826.

DOI: 10.1016/j.dss.2006.12.011

[10] Zhang D, Zhou X, Leung S C H, et al. Vertical bagging decision trees model for credit scoring. Expert Systems with Applications, 2010, 37(12): 7838-7843.

DOI: 10.1016/j.eswa.2010.04.054