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

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  • Asogbon Mojisola Grace

    (Department of Computer Science, Federal University of Technology Akure, Akure, Nigeria)

  • Samuel Oluwarotimi Williams

    (Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China)

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.

Suggested Citation

  • Asogbon Mojisola Grace & Samuel Oluwarotimi Williams, 2016. "Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 12(1), pages 47-62, January.
    • Handle: RePEc:igg:jiit00:v:12:y:2016:i:1:p:47-62
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    Cited by:

    1. Nadia Ayed & Khemaies Bougatef, 2024. "Performance Assessment of Logistic Regression (LR), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS) in Predicting Default Probability: The Case of," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1803-1835, September.

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