Credit scoring using the hybrid neural discriminant technique

Lee, Tian–Shyug; Chiu, Chih‐Chou; Lu, Chi-Jie; Chen, I-Fei

doi:10.1016/s0957-4174(02)00044-1

Cited by 286 publications

(197 citation statements)

References 41 publications

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“…Discriminant analysis has been used to solve classifi cation problems for fi nance, business, and marketing research (Lee et al 1997;Kim et al 2000;Trevino and Daniels 1995). For credit scoring problems, several researchers have proposed and used the discriminant analysis and its variations (Lee et al 2002;Lee et al 2006).…”

Section: Methodsmentioning

confidence: 99%

“…The statistical methods, nonparametric statistical methods, and artifi cial intelligence approaches have been proposed to support the credit decision (Thomas 2000). Credit scoring problems are basically in the domain of the more general and widely discussed classifi cation problems (Lee et al 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Statistical methods, including regression, linear and nonlinear discriminant analysis, logit and probit models were most commonly applied to construct credit scoring models (Vojtek and Kočenda 2006;Lee et al 2002;Lee et al 2006). The most popular methods applied to credit scoring models are linear discriminant analysis, logistic regression and their variations.…”

Section: Introductionmentioning

confidence: 99%

“…Practitioners and researchers have developed a variety of techniques for credit scoring, which involve k-nearest neighbor (Henley and Hand 1996), decision trees (Lee et al 2006), neural networks (Lee et al 2002;Malhotra, R. and Malhotra, D. K. 2002;West 2000), and genetic programming (Ong et al 2005), support vector machines models. These techniques can be used as an alternative to discriminant analysis and logistic regression, in situations where the dependent and independent variables exhibit complex nonlinear relationships (Lee et al 2006).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Comparison of Data Mining Techniques for Credit Scoring in Banking: A Managerial Perspective

İnce

Aktan

2009

Journal of Business Economics and Management

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Abstract. Credit scoring is a very important task for lenders to evaluate the loan applications they receive from consumers as well as for insurance companies, which use scoring systems today to evaluate new policyholders and the risks these prospective customers might present to the insurer. Credit scoring systems are used to model the potential risk of loan applications, which have the advantage of being able to handle a large volume of credit applications quickly with minimal labour, thus reducing operating costs, and they may be an effective substitute for the use of judgment among inexperienced loan offi cers, thus helping to control bad debt losses. This study explores the performance of credit scoring models using traditional and artifi cial intelligence approaches: discriminant analysis, logistic regression, neural networks and classifi cation and regression trees. Experimental studies using real world data sets have demonstrated that the classifi cation and regression trees and neural networks outperform the traditional credit scoring models in terms of predictive accuracy and type II errors.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of Data Mining Techniques for Credit Scoring in Banking: A Managerial Perspective

İnce

Aktan

2009

Journal of Business Economics and Management

View full text Add to dashboard Cite

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“…These techniques leads to an uncertainty described as the classification problem which should be improved [2]. Credit scoring models have been widely used by financial institutions to determine if loan customers belong to either a good applicant group or a bad applicant group .The advantages of using credit scoring models can be described as the benefit from reducing the cost of credit analysis, enabling faster credit decision, insuring credit collections, and diminishing possible risk [3,4]. Since an improvement in accuracy of a fraction of a percent might translate into significant savings [4], a more sophisticated model should be proposed to significantly improve the accuracy of the credit scoring model in this paper.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Labeling the Class of Bank Credit Customers via Classification Method in Data Mining

Nosratabadi¹,

Pourdarab²,

Nadali³

2011

IJIET

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Abstract-ONE of the most important parts in credit scoring is determining the class of customers to run the Data Mining algorithms. The purpose of this research is estimating the Label of Credit customers via Fuzzy Expert System. Here the class of customers has been specified by a Fuzzy Expert System and then the Data Mining Algorithms have been run on the final data with Clementine software. The presented steps have been studied in an Iranian Bank as empirical study.

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A bi‐level neural‐based fuzzy classification approach for credit scoring problems

et al. 2013

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The credit scoring is a risk evaluation task considered as a critical decision for financial institutions in order to avoid wrong decision that may result in huge amount of losses. Classification models are one of the most widely used groups of data mining approaches that greatly help decision makers and managers to reduce their credit risk of granting credits to customers instead of intuitive experience or portfolio management. Accuracy is one of the most important criteria in order to choose a credit‐scoring model; and hence, the researches directed at improving upon the effectiveness of credit scoring models have never been stopped. In this article, a hybrid binary classification model, namely FMLP, is proposed for credit scoring, based on the basic concepts of fuzzy logic and artificial neural networks (ANNs). In the proposed model, instead of crisp weights and biases, used in traditional multilayer perceptrons (MLPs), fuzzy numbers are used in order to better model of the uncertainties and complexities in financial data sets. Empirical results of three well‐known benchmark credit data sets indicate that hybrid proposed model outperforms its component and also other those classification models such as support vector machines (SVMs), K‐nearest neighbor (KNN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA). Therefore, it can be concluded that the proposed model can be an appropriate alternative tool for financial binary classification problems, especially in high uncertainty conditions. © 2013 Wiley Periodicals, Inc. Complexity 18: 46–57, 2013

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Credit scoring using the hybrid neural discriminant technique

Cited by 286 publications

References 41 publications

A Comparison of Data Mining Techniques for Credit Scoring in Banking: A Managerial Perspective

A Comparison of Data Mining Techniques for Credit Scoring in Banking: A Managerial Perspective

A New Approach for Labeling the Class of Bank Credit Customers via Classification Method in Data Mining

A bi‐level neural‐based fuzzy classification approach for credit scoring problems

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