A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines

Lee, Tian–Shyug; Chen, I-Fei

doi:10.1016/j.eswa.2004.12.031

Cited by 321 publications

(187 citation statements)

References 65 publications

Supporting

Mentioning

184

Contrasting

Unclassified

Order By: Relevance

“…Therefore, the quadratic discriminant analysis (QDA) needs to be used. Despite the fact that LDA is only a special case of QDA with stronger assumptions, LDA has been reported to be a more robust method when the theoretical presumptions are violated (Lee and Chen 2005). 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).…”

Section: Methodsmentioning

confidence: 99%

“…Several artifi cial intelligence techniques, which are decision trees, neural networks, genetic programming, k-nearest neighbor models, have been developed by practitioners and researchers for credit scoring (Malhotra, R. and Malhotra, D. K. 2002;West 2000, Ong et al 2005Lee et al 2006;Lee and Chen 2005;Lee et al 2002). In this study, we will develop and compare credit scoring models based on neural networks, and classifi cation and regression trees.…”

Section: Artifi Cial Intelligence Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Artifi Cial Intelligence Techniquesmentioning

confidence: 99%

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

show abstract

“…MARS has also been applied to predict the average monthly foreign exchange rates [12], to model credit scoring [13], and for data mining on breast cancer pattern [14]. In all of the cited studies, promising results have been reported where MARS has been employed either for forecasting or for data mining purposes.…”

Section: A Marsmentioning

confidence: 91%

Prediction of Recurrence in Patients with Cervical Cancer Using MARS and Classification

Chang¹,

Cheng²,

Lu³

et al. 2013

IJMLC

View full text Add to dashboard Cite

show abstract

“…However, the misclassification costs are seldom available because the estimate of their values is a complex and challenging task (Lee and Chen 2005). In fact, only 5% of the papers reviewed have included the expected misclassification cost in their experimental protocol.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

2014

View full text Add to dashboard Cite

Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000-2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.

show abstract

A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines

Cited by 321 publications

References 65 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

Prediction of Recurrence in Patients with Cervical Cancer Using MARS and Classification

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Contact Info

Product

Resources

About