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Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation

Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation

Asogbon Mojisola Grace, Samuel Oluwarotimi Williams
Copyright: © 2017 |Pages: 17
ISBN13: 9781522507888|ISBN10: 1522507884|EISBN13: 9781522507895
DOI: 10.4018/978-1-5225-0788-8.ch048
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MLA

Grace, Asogbon Mojisola, and Samuel Oluwarotimi Williams. "Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation." Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1289-1305. https://doi.org/10.4018/978-1-5225-0788-8.ch048

APA

Grace, A. M. & Williams, S. O. (2017). Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation. In I. Management Association (Ed.), Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications (pp. 1289-1305). IGI Global. https://doi.org/10.4018/978-1-5225-0788-8.ch048

Chicago

Grace, Asogbon Mojisola, and Samuel Oluwarotimi Williams. "Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation." In Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1289-1305. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0788-8.ch048

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Abstract

Credit risk evaluation techniques that aid effective decisions in credit lending are of great importance to the financial and banking industries. Such techniques assist credit managers to minimize the risks often associated with wrong decision making. Several techniques have been developed in the time past for credit risk evaluation and these techniques suffer from one form of limitation or the other. Recently, powerful soft computing tools have been proposed for problem solving among which are the neural networks and fuzzy logic. In this study, a neural network based on backpropagation learning algorithm and a fuzzy inference system based on Mamdani model were developed to evaluate the risk level of credit applicants. A comparative analysis of the performances of both systems was carried out and experimental results show that neural network with an overall prediction accuracy of 96.89% performed better than the fuzzy logic method with 94.44%. Finding from this study could provide useful information on how to improve the performance of existing credit risk evaluation systems.

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