A systematic credit scoring model based on heterogeneous classifier ensembles

Lending loans to borrowers is considered one of the main profit sources for banks and financial institutions. Thus, careful assessment and evaluation should be taken when deciding to grant credit to potential borrowers. With the rapid growth of credit industry and the massive volume of financial data, developing effective credit scoring models is very crucial. The literature in this area is very dense with models that aim to get the best predictive performance. Recent studies stressed on using ensemble models or multiple classifiers over single ones to solve credit scoring problems. Therefore, this study propose to develop and introduce a systematic credit scoring model based on homogenous and heterogeneous classifier ensembles based on three state-of-the art classifiers: logistic regression (LR), artificial neural network (ANN) and support vector machines (SVM). Results revealed that heterogeneous classifier ensembles gives better predictive performance than homogenous and single classifiers in terms of average accuracy.

[1]  M. Abbod,et al.  Evaluating Consumer Loans Using Neural Networks Ensembles , 2022 .

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

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  S. Haykin.  Adaptive Filters , 2007 .

[5]  Jian Ma,et al.  Two credit scoring models based on dual strategy ensemble trees , 2012, Knowl. Based Syst..

[6]  Bhekisipho Twala,et al.  Multiple classifier application to credit risk assessment , 2010, Expert Syst. Appl..

[7]  Chih-Fong Tsai,et al.  Using neural network ensembles for bankruptcy prediction and credit scoring , 2008, Expert Syst. Appl..

[8]  Herbert L. Jensen,et al.  Using Neural Networks for Credit Scoring , 1992 .

[9]  Wuyi Yue,et al.  Support vector machine based multiagent ensemble learning for credit risk evaluation , 2010, Expert Syst. Appl..

[10]  Ali H. Sayed,et al.  Adaptive Filters , 2008 .

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

[12]  Loris Nanni,et al.  An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring , 2009, Expert Syst. Appl..

[13]  Lun-Ping Hung,et al.  A data driven ensemble classifier for credit scoring analysis , 2010, Expert Syst. Appl..

[14]  José Salvador Sánchez,et al.  Two-level classifier ensembles for credit risk assessment , 2012, Expert Syst. Appl..

[15]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

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

[17]  David J. Hand,et al.  Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .

[18]  Soner Akkoç,et al.  An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data , 2012, Eur. J. Oper. Res..

[19]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

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

[21]  Kin Keung Lai,et al.  Least squares support vector machines ensemble models for credit scoring , 2010, Expert Syst. Appl..

[22]  Kin Keung Lai,et al.  Credit risk assessment with a multistage neural network ensemble learning approach , 2008, Expert Syst. Appl..

[23]  Ying Wah Teh,et al.  Credit Scoring Models Using Soft Computing Methods: A Survey , 2010, Int. Arab J. Inf. Technol..

[24]  A. I. Marqués,et al.  Exploring the behaviour of base classifiers in credit scoring ensembles , 2012, Expert Syst. Appl..

[25]  Jian Ma,et al.  A comparative assessment of ensemble learning for credit scoring , 2011, Expert Syst. Appl..

[26]  Johan A. K. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring , 2003, J. Oper. Res. Soc..

[27]  Steven Finlay,et al.  Multiple classifier architectures and their application to credit risk assessment , 2011, Eur. J. Oper. Res..

[28]  Amir F. Atiya,et al.  Bankruptcy prediction for credit risk using neural networks: A survey and new results , 2001, IEEE Trans. Neural Networks.

[29]  Chih-Fong Tsai,et al.  Combining cluster analysis with classifier ensembles to predict financial distress , 2014, Inf. Fusion.

[30]  Adnan Khashman,et al.  Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes , 2010, Expert Syst. Appl..

[31]  David West,et al.  Neural network ensemble strategies for financial decision applications , 2005, Comput. Oper. Res..

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

[33]  Tian-Shyug Lee,et al.  A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines , 2005, Expert Syst. Appl..

[34]  Jonathan Crook,et al.  Support vector machines for credit scoring and discovery of significant features , 2009, Expert Syst. Appl..

[35]  Hongle Du,et al.  A Cooperative Network Intrusion detection Based on Fuzzy SVMs , 2010, J. Networks.

[36]  Mu-Chen Chen,et al.  Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..

[37]  T. Dinh,et al.  A credit scoring model for Vietnam's retail banking market , 2007 .

[38]  Arturo Estrella.  CREDIT RATINGS AND COMPLEMENTARY SOURCES OF CREDIT QUALITY INFORMATION , 2000 .

[39]  R. Malhotra,et al.  Evaluating Consumer Loans Using Neural Networks , 2001 .

[40]  Stephen C. H. Leung,et al.  Vertical bagging decision trees model for credit scoring , 2010, Expert Syst. Appl..

[41]  J. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research , 2015, Eur. J. Oper. Res..

[42]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[43]  Chih-Fong Tsai,et al.  Credit rating by hybrid machine learning techniques , 2010, Appl. Soft Comput..

[44]  Alfredo Vellido,et al.  Neural networks in business: a survey of applications (1992–1998) , 1999 .

[45]  B. Baesens,et al.  A support vector machine approach to credit scoring , 2003 .

[46]  Hussein A. Abdou,et al.  Credit scoring and decision making in Egyptian public sector banks , 2009 .

[47]  C ONG,et al.  Building credit scoring models using genetic programming , 2005, Expert Syst. Appl..

[48]  Kin Keung Lai,et al.  A new fuzzy support vector machine to evaluate credit risk , 2005, IEEE Transactions on Fuzzy Systems.

[49]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .