An Overview of Personal Credit Scoring: Techniques and Future Work

Xiao-Lin Li, Yu Zhong

School of Management, Nanjing University, Nanjing, China.

DOI: 10.4236/ijis.2012.224024                

Abstract

Personal credit scoring is the application of financial risk forecasting. It becomes an even important task as financial institutions have been experiencing serious competition and challenges. In this paper, the techniques used for credit scoring are summarized and classified and the new method—ensemble learning model is introduced. This article also discusses some problems in current study. It points out that changing the focus from static credit scoring to dynamic behavioral scoring and maximizing revenue by decreasing the Type I and Type II error are two issues in current study. It also suggested that more complex models cannot always been applied to actual situation. Therefore, how to use the assessment models widely and improve the prediction accuracy is the main task for future research.

Keywords

Credit Scoring; Ensemble Learning; Dynamic Behavioral Scoring; Type I and Type II Error

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	L. C. Thomas, “A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers,” International Journal of Forecasting, Vol. 16, No. 2, 2000, pp. 149-172.
[2]	D. Durand, “Risk Elements in Consumer Instatement Financing,” National Bureau of Economic Research, New York, 1941.
[3]	R. A. Fisher, “The Use of Multiple Measurements in Taxonomic Problems,” Annuals of Eugenices, Vol. 2, No. 7, 1936, pp. 179-188.
[4]	S. L. Lin, “A New Two-Stage Hybrid Approach of Credit Risk in Banking Industry,” Expert Systems with Applications, Vol. 36, No. 4, 2009, pp. 8333-8341.
[5]	Friedman, J. H. (1991). Multivariate adaptive regression splines. Annals of Statistics 19(1), 1-141.
[6]	S. Sarkar and R. S. Sriram, “Bayesian Models for Early Warning of Bank Failures,” Management Science, Vol. 47, No. 11, 2001, pp. 1457-1475.
[7]	L. Sun and P. P. Shenoy, “Using Bayesian Networks for Bankruptcy Prediction: Some Methodological Issues,” European Journal of Operational Research, Vol. 180, No. 2, 2007, pp. 738-753.
[8]	K. Liu, K. K. Lai and S.-M. Guu, “Dynamic Credit Scoring on Consumer Behavior Using Fuzzy Markov Model,” 2009 Fourth International Multi-Conference on Computing in the Global Information Technology, Cannes, 2009, pp. 235-239.
[9]	S. C. Wang, C. P. Leng and P. Q. Zhang, “Conditional Markov Network Hybrid Classifiers Using on Client Credit Scoring,” 2008 International Symposium on Computer Science and Computational Technology, Shanghai, 2008, pp. 549-553.
[10]	H. Frydman and T. Schuermann, “Credit Rating Dynamics and Markov Mixture Models,” Journal of Banking & Finance, Vol. 32, No. 6, 2008, pp. 1062-1074.
[11]	H. Abdou, J. Pointon and A. E. Masry, “Neural Nets Versus Conventional Techniques in Credit Scoring in Egyptian Banking,” Expert Systems with Applications, Vol. 35, No. 2, 2008, pp. 1275-1292.
[12]	V. S. Desai, J. N. Crook and G. A. Overstreet, “A Comparison of Neural Networks and Linear Scoring Models in the Credit Union Environment,” European Journal of Operational Research, Vol. 95, No. 1, 1996, pp. 24-47.
[13]	R. Malhotra and D. K. Malhotra, “Differentiating between Good Credits and Bad Credits Using Neuro-Fuzzy Systems,” European Journal of Operational Research, Vol. 136, No. 1, 2002, pp. 190-211.
[14]	Z. Huang, H. Chen, C. J. Hsu, W. H. Chen and S. Wu, “Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study,” Decision Support System, Vol. 37, No. 4, 2004, pp. 543-558.
[15]	Y. X. Yang, “Adaptive Credit Scoring with Kernel Learning Methods,” European Journal of Operational Research, Vol. 183, No. 3, 2007, pp. 1521-1536.
[16]	H. S. Kim and S. Y. Sohn, “Support Vector Machines for Default Prediction of SMEs Based on Technology Credit,” European Journal of Operational Research, Vol. 201, No. 3, 2010, pp. 838-846.
[17]	J. Holland, “Genetic Algorithms,” Scientific American, Vol. 267, No. 1, 1992, pp. 66-72.
[18]	J. R. Koza, “Genetic Programming on the Programming of Computers by Means of Natural Selection,” The MIT Press, Cambridge, 1992.
[19]	H. A. Abdou, “Genetic Programming for Credit Scoring: The Case of Egyptian Public Sector Banks,” Expert Systems with Applications, Vol. 36, No. 9, 2009, pp. 11402- 11417.
[20]	C. S. Ong, J. J. Huang and G. H. Tzengb, “Building Credit Scoring Models Using Genetic Programming,” Expert Systems with Applications, Vol. 29, No. 1, 2005, pp. 41-47.
[21]	J. Arroyo and C. Maté, “Forecasting Histogram Time Series with K-Nearest Neighbours Methods,” International Journal of Forecasting, Vol. 25, No. 1, 2009, pp. 192-207.
[22]	J. T. S. Quah and M. Sriganesh, “Real-Time Credit Card Fraud Detection Using Computational Intelligence,” Expert Systems with Applications, Vol. 35, No. 4, 2008, pp. 1721-1732.
[23]	S. T. Luo, B. W. Cheng and C. H. Hsieh, “Prediction Model Building with Clustering-Launched Classification And Support Vector Machines in Credit Scoring,” Expert Systems with Application, Vol. 36, No. 4, 2009, pp. 7562-7566.
[24]	C. F. Tsai and M. L. Chen, “Credit Rating by Hybrid Machine Learning Techniques,” Applied Soft Computing, Vol. 10, No. 2, 2010, pp. 374-380.
[25]	T. S. Lee and I. F. Chen, “A Two-Stage Hybrid Credit Scoring Model Using Artificial Neural Networks and Multivariate Adaptive Regression Splines,” Expert Systems with Application, Vol. 28, No. 4, 2005, pp. 743-752.
[26]	M. ?u?ter?ic, D. Mramor and J. Zupan, “Consumer Credit Scoring Models with Limited Data,” Expert Systems with Applications, Vol. 36, No. 3, 2009, pp. 4736-4744.
[27]	C. L. Huang, M. C. Chen and C. J. Wang, “Credit Scoring with a Data Mining Approach Based on Support Vector Machines,” Expert Systems with Application, Vol. 33, No. 4, 2007, pp. 847-856.
[28]	W. M. Chen, C. Q. Ma and L. Ma, “Mining the Customer Credit Using Hybrid Support Vector Machine Technique,” Expert Systems with Applications, Vol. 36, No. 4, 2009, pp. 7611-7616.
[29]	P. Yao, “Hybrid Classifier Using Neighborhood Rough Set and SVM for Credit Scoring,” 2009 International Conference on Business Intelligence and Financial Engineering, Beijing, 2009, pp. 138-142.
[30]	N. C. Hsieh and L. P. Hung, “A Data Driven Ensemble Classi?er for Credit Scoring Analysis,” Expert Systems with Application, Vol. 37, No. 1, 2010, pp. 534-545.
[31]	N. C. Hsieh, “Hybrid Mining Approach in the Design of Credit Scoring Models,” Expert Systems with Application, Vol. 28, No. 4, 2005, pp. 655-665.
[32]	G. Paleologo, A. Elisseeff and G. Antonini, “Subagging for Credit Scoring Models,” European Journal of Operational Research, Vol. 201, No. 1, 2010, pp. 490-499.
[33]	L. Yu, S. Y. Wang and K. K. Lai, “Credit Risk Assessment with a Multistage Neural Network Ensemble Learning Approach,” Expert Systems with Application, Vol. 34, No. 2, 2008, pp. 1434-1444.
[34]	L. Zhoua, K. K. Laia and L. Yub, “Least Squares Support Vector Machines Ensemble Models for Credit Scoring,” Expert Systems with Applications, Vol. 37, No. 1, 2010, pp. 127-133.
[35]	L. Nanni and A. Lumini, “An Expermental Comparison of Ensemble of Classifiers of Bankruptcy Prediction and Credit Scoring,” Expert Systems with Applications, Vol. 36, No. 2, 2009, pp. 3028-3033.
[36]	N. C. Hsieh, “An Integrated Data Mining and Behavioral Scoring Model for Analyzing Bank Customers,” Expert Systems with Applications, Vol. 27, No. 4, 2004, pp. 623-633.
[37]	C. L. Chuang and R. H. Lin, “Constructing a Reassigning Credit Scoring Model,” Expert Systems with Application, Vol. 36, No. , 2009, pp. 1685-1694.
[38]	E. Angelini, G. D. Tollo and A. Roli, “A Neural Network Approach for Credit Risk Evaluation,” The Quarterly Review of Economics and Finance, Vol. 48, No. 4, 2008, pp. 733-755.
[39]	D. J. Hand, “Mining the Past to Determine the Future: Problems and Possibilities,” International Journal of Forecasting, Vol. 25, No. 3, 2009, pp. 441-451.