The consumer loan default predicting model - An application of DEA-DA and neural network

In this paper we construct the consumer loan default predicting model through conducting the empirical analysis on the customers of unsecured consumer loan from a certain financial institution in Taiwan, and adopt the borrower's demographic variables and money attitude as the real-timeaneous discriminant information. Furthermore, we construct respectively through four predicting methods, such as DA, LR, NN and DEA-DA, to compare the suitability of these four mentioned methods. The results show that DEA-DA and NN are possessed better predicting capability and they are the optimal predicting model that this study longing for. In addition, this study showed that the default loan predicting model will be possessed higher level of predicting capability after added money attitude.

[1]  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..

[2]  Eli Jones,et al.  Money Attitudes, Credit Card Use, and Compulsive Buying among American College Students. , 2001 .

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

[4]  Jonathan Crook,et al.  Does reject inference really improve the performance of application scoring models , 2004 .

[5]  Toshiyuki Sueyoshi,et al.  Financial Ratio Analysis of the Electric Power Industry , 2005, Asia Pac. J. Oper. Res..

[6]  Thompson S. H. Teo,et al.  Sex, money and financial hardship: An empirical study of attitudes towards money among undergraduates in Singapore , 1997 .

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

[8]  John L. Adrian,et al.  A linear programming alternative to discriminant analysis in credit scoring , 1985 .

[9]  H. Frydman,et al.  Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress , 1985 .

[10]  James A. Roberts,et al.  Demographics and money attitudes: a test of Yamauchi and Templers (1982) money attitude scale in Mexico , 1999 .

[11]  Pamela R. Turner,et al.  Discriminating the number of credit cards held by college students using credit and money attitudes , 1999 .

[12]  H NOH,et al.  Prognostic personal credit risk model considering censored information , 2005, Expert Syst. Appl..

[13]  Kasper Roszbach.  Bank Lending Policy, Credit Scoring and the Survival of Loans , 2004 .

[14]  David Lando,et al.  Credit Risk Modeling , 2009 .

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

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

[17]  R. Avery,et al.  Consumer Credit Scoring: Do Situational Circumstances Matter? , 2004 .

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

[19]  Toshiyuki Sueyoshi,et al.  A Use of nonparametric Tests for DEA-Discriminant Analysis: a Methodological Comparison , 2004, Asia Pac. J. Oper. Res..

[20]  A. Steenackers,et al.  A credit scoring model for personal loans , 1989 .

[21]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[22]  K. Yamauchi,et al.  The development of a Money Attitude Scale. , 1982, Journal of personality assessment.

[23]  Toshiyuki Sueyoshi,et al.  Mixed integer programming approach of extended DEA-discriminant analysis , 2004, Eur. J. Oper. Res..

[24]  E. Mine Cinar,et al.  Neural Networks: A New Tool for Predicting Thrift Failures , 1992 .

[25]  Toshiyuki Sueyoshi,et al.  DEA-discriminant analysis in the view of goal programming , 1999, Eur. J. Oper. Res..

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

[27]  Haiyang Chen,et al.  An analysis of personal financial literacy among college students , 1998 .

[28]  Sheng-Tun Li,et al.  The evaluation of consumer loans using support vector machines , 2006, Expert Syst. Appl..

[29]  Toshiyuki Sueyoshi,et al.  DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches , 2006, Eur. J. Oper. Res..

[30]  Joy M. Jacobs-Lawson,et al.  Influence of future time perspective, financial knowledge, and financial risk tolerance on retirement saving behaviors , 2005 .

[31]  Ming Liu,et al.  Residential Mortgage Lending and Borrower Risk: The Relationship Between Mortgage Spreads and Individual Characteristics , 2002 .

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

[33]  Bart Baesens,et al.  Neural network survival analysis for personal loan data , 2005, J. Oper. Res. Soc..

[34]  Chih-Chou Chiu,et al.  Credit scoring using the hybrid neural discriminant technique , 2002, Expert Syst. Appl..

[35]  Tian-Shyug Lee,et al.  Mining the customer credit using classification and regression tree and multivariate adaptive regression splines , 2006, Comput. Stat. Data Anal..

[36]  Thompson S. H. Teo,et al.  Sex, financial hardship and locus of control: an empirical study of attitudes towards money among Singaporean Chinese , 2003 .