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2018 | OriginalPaper | Chapter

A Hybrid Model of AdaBoost and Back-Propagation Neural Network for Credit Scoring

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Abstract

Owing to the development of internet finance in China, credit scoring is growing into one of the most important issues in the field of financial risk management. Quantitative credit scoring models are widely used tools for credit risk assessment in financial institutions. In this study, an AdaBoost algorithm model based on back-propagation neural network for credit scoring with high accuracy and efficiency is proposed. We first illustrate the basic concepts of back-propagation neural network and AdaBoost algorithm and propose a hybrid model of AdaBoost and back-propagation neural network, then two real-world credit data sets are selected to demonstrate the effectiveness and feasibility of the proposed model. The results show that the proposed model can get higher accuracy compared to other classifiers listed in this study.

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Literature
1.
go back to reference Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finan 23(4):589–609CrossRef Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finan 23(4):589–609CrossRef
2.
go back to reference Baesens B, van Gestel T et al (2003) Benchmarking state-of-art classification algorithms for credit scoring. Oper Res Soc 54(6):627–635CrossRefMATH Baesens B, van Gestel T et al (2003) Benchmarking state-of-art classification algorithms for credit scoring. Oper Res Soc 54(6):627–635CrossRefMATH
3.
go back to reference Baesens B, Setiono R et al (2003) Using neural network rule extraction and decision tables for credit-risk evaluation. Manag Sci 49(3):312–329CrossRefMATH Baesens B, Setiono R et al (2003) Using neural network rule extraction and decision tables for credit-risk evaluation. Manag Sci 49(3):312–329CrossRefMATH
4.
go back to reference Beynon MJ, Peel MJ (2001) Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega 29(6):561–576CrossRef Beynon MJ, Peel MJ (2001) Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega 29(6):561–576CrossRef
5.
go back to reference Carter C, Catlett J (1987) Assessing credit card applications using machine learning. IEEE Expert 2(3):71–79CrossRef Carter C, Catlett J (1987) Assessing credit card applications using machine learning. IEEE Expert 2(3):71–79CrossRef
6.
go back to reference Chen M, Ma L, Gao Y (2010) Vehicle detection segmentation based on adaboost and grabcut. In: IEEE International Conference on Progress in Informatics and Computing, pp 896–900 Chen M, Ma L, Gao Y (2010) Vehicle detection segmentation based on adaboost and grabcut. In: IEEE International Conference on Progress in Informatics and Computing, pp 896–900
7.
go back to reference Chen MC, Huang SH (2003) Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Syst Appl 24(4):433–441CrossRef Chen MC, Huang SH (2003) Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Syst Appl 24(4):433–441CrossRef
8.
go back to reference Desai VS, Crook JN, Overstreet J (1996) A comparison of neural networks and linear scoring models in the credit union environment. Oper Res 95(2):24–37CrossRefMATH Desai VS, Crook JN, Overstreet J (1996) A comparison of neural networks and linear scoring models in the credit union environment. Oper Res 95(2):24–37CrossRefMATH
9.
go back to reference Essa EM, Tolba AS, Elmougy S (2008) A comparison of combined classifier architectures for arabic speech recognition. In: International Conference on Computer Engineering & Systems, pp 149–153 Essa EM, Tolba AS, Elmougy S (2008) A comparison of combined classifier architectures for arabic speech recognition. In: International Conference on Computer Engineering & Systems, pp 149–153
10.
go back to reference Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory, pp 119–139 Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory, pp 119–139
11.
go back to reference Gestel TV, Baesens B et al (2003) A support vector machine approach to credit scoring. Banken Financiewezen 2:73–82 Gestel TV, Baesens B et al (2003) A support vector machine approach to credit scoring. Banken Financiewezen 2:73–82
12.
go back to reference Henley WE, Hand DJ (1996) A k-nearest-neighbour classifier for assessing consumer credit risk. J Roy Stat Soc 45(1):77–95 Henley WE, Hand DJ (1996) A k-nearest-neighbour classifier for assessing consumer credit risk. J Roy Stat Soc 45(1):77–95
13.
go back to reference Henley WE, Hand DJ (1997) Construction of a k-nearest-neighbour credit-scoring system. IMA J Manag Math 8(4):305–321CrossRefMATH Henley WE, Hand DJ (1997) Construction of a k-nearest-neighbour credit-scoring system. IMA J Manag Math 8(4):305–321CrossRefMATH
14.
go back to reference Khashman A (2009) A neural network model for credit risk evaluation. Int J Neural Syst 19(4):285–294CrossRef Khashman A (2009) A neural network model for credit risk evaluation. Int J Neural Syst 19(4):285–294CrossRef
15.
go back to reference Li H, Sun J (2008) Ranking-order case-based reasoning for financial distress prediction. Knowl Based Syst 21(8):868–878CrossRef Li H, Sun J (2008) Ranking-order case-based reasoning for financial distress prediction. Knowl Based Syst 21(8):868–878CrossRef
16.
go back to reference Li H, Sun J (2009) Gaussian case-based reasoning for business failure prediction with empirical data in China. Inf Sci 179(1):89–108CrossRef Li H, Sun J (2009) Gaussian case-based reasoning for business failure prediction with empirical data in China. Inf Sci 179(1):89–108CrossRef
17.
go back to reference Li H, Sun J (2009) Predicting business failure using multiple case-based reasoning combined with support vector machine. Expert Syst Appl 36(6):10085–10096CrossRef Li H, Sun J (2009) Predicting business failure using multiple case-based reasoning combined with support vector machine. Expert Syst Appl 36(6):10085–10096CrossRef
18.
go back to reference Li H, Sun J (2010) Business failure prediction using hybrid2 case-based reasoning (h2cbr). Comput Oper Res 37(1):137–151CrossRef Li H, Sun J (2010) Business failure prediction using hybrid2 case-based reasoning (h2cbr). Comput Oper Res 37(1):137–151CrossRef
19.
go back to reference Li H, Sun J, Sun BL (2009) Financial distress prediction based on or-cbr in the principle of k-nearest neighbors. Expert Syst Appl 36(1):643–659CrossRef Li H, Sun J, Sun BL (2009) Financial distress prediction based on or-cbr in the principle of k-nearest neighbors. Expert Syst Appl 36(1):643–659CrossRef
20.
go back to reference Malhotra R, Malhotra DK (2002) Differentiating between good credits and bad credits using neuro-fuzzy systems. Eur J Oper Res 136(1):190–211CrossRefMATH Malhotra R, Malhotra DK (2002) Differentiating between good credits and bad credits using neuro-fuzzy systems. Eur J Oper Res 136(1):190–211CrossRefMATH
21.
go back to reference Malhotra R, Malhotra DK (2003) Evaluating consumer loans using neural networks. Soc Sci Electron Publishing 31(2):83–96 Malhotra R, Malhotra DK (2003) Evaluating consumer loans using neural networks. Soc Sci Electron Publishing 31(2):83–96
22.
go back to reference Ong CS, Huang JJ, Tzeng GH (2005) Building credit scoring models using genetic programming. Expert Syst Appl 29(1):41–47CrossRef Ong CS, Huang JJ, Tzeng GH (2005) Building credit scoring models using genetic programming. Expert Syst Appl 29(1):41–47CrossRef
23.
go back to reference Piramuthu S, Piramuthu S (1999) Financial credit-risk evaluation with neural and neurofuzzy systems. Eur J Oper Res 112(2):310–321MathSciNetCrossRefMATH Piramuthu S, Piramuthu S (1999) Financial credit-risk evaluation with neural and neurofuzzy systems. Eur J Oper Res 112(2):310–321MathSciNetCrossRefMATH
24.
go back to reference Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536CrossRef Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536CrossRef
25.
go back to reference Schebesch KB, Stecking R (2005) Support vector machines for classifying and describing credit applicants: detecting typical and critical regions. J Oper Res Soc 56(9):1082–1088CrossRefMATH Schebesch KB, Stecking R (2005) Support vector machines for classifying and describing credit applicants: detecting typical and critical regions. J Oper Res Soc 56(9):1082–1088CrossRefMATH
26.
go back to reference Steenackers A, Goovaerts MJ (1989) A credit scoring model for personal loans. Insur Math Econ 8(1):31–34CrossRef Steenackers A, Goovaerts MJ (1989) A credit scoring model for personal loans. Insur Math Econ 8(1):31–34CrossRef
27.
go back to reference Thomas LC, Edelman DB, Crook JN (2002) Credit scoring and its applications. SIAM, PhiladelphiaCrossRefMATH Thomas LC, Edelman DB, Crook JN (2002) Credit scoring and its applications. SIAM, PhiladelphiaCrossRefMATH
28.
go back to reference Varetto F (1951) Genetic algorithms in the analysis of insolvency risk. University of Illinois Press, Champaign Varetto F (1951) Genetic algorithms in the analysis of insolvency risk. University of Illinois Press, Champaign
29.
go back to reference Wang Y, Wang S, Lai KK (2006) A new fuzzy support vector machine to evaluate credit risk. IEEE Trans Fuzzy Syst 13(6):820–831MathSciNetCrossRef Wang Y, Wang S, Lai KK (2006) A new fuzzy support vector machine to evaluate credit risk. IEEE Trans Fuzzy Syst 13(6):820–831MathSciNetCrossRef
30.
31.
32.
go back to reference Yu L, Wang S, Lai KK (2008) Credit risk assessment with a multistage neural network ensemble learning approach. Expert Syst Appl 34(2):1434–1444CrossRef Yu L, Wang S, Lai KK (2008) Credit risk assessment with a multistage neural network ensemble learning approach. Expert Syst Appl 34(2):1434–1444CrossRef
33.
go back to reference Yu L, Wang SY et al (2008) Designing a hybrid intelligent mining system for credit risk evaluation. Syst Sci Complex 21(5):527–539MathSciNetCrossRefMATH Yu L, Wang SY et al (2008) Designing a hybrid intelligent mining system for credit risk evaluation. Syst Sci Complex 21(5):527–539MathSciNetCrossRefMATH
34.
go back to reference Yu L, Wang S, Cao J (2009) A modified least squares support vector machine classifier with application to credit risk analysis. Int J Inf Technol Decis Making 08(4):697–710CrossRefMATH Yu L, Wang S, Cao J (2009) A modified least squares support vector machine classifier with application to credit risk analysis. Int J Inf Technol Decis Making 08(4):697–710CrossRefMATH
35.
go back to reference Zhou L, Lai KK, Yu L (2009) Credit scoring using support vector machines with direct search for parameters selection. Soft Comput 13(2):149–155CrossRefMATH Zhou L, Lai KK, Yu L (2009) Credit scoring using support vector machines with direct search for parameters selection. Soft Comput 13(2):149–155CrossRefMATH
Metadata
Title
A Hybrid Model of AdaBoost and Back-Propagation Neural Network for Credit Scoring
Authors
Feng Shen
Xingchao Zhao
Dao Lan
Limei Ou
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-319-59280-0_6

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