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

Abstract

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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“…Specifically, the network computes the joint probability density function of U and X. The expected value of X given U is expressed as [9]:…”

Financial distress prediction using the hybrid associative memory with translation

“…Khashei et al [35] employed basic concepts of fuzzy logic and MLP neural networks to implement a hybrid binary credit risk prediction model, where fuzzy numbers were used so that the uncertainties and complexities in financial data sets can be better modeled. The emotional neural networks were successfully applied to credit scoring and evaluation [37], showing higher accuracy and lower computing time than the conventional neural models based on the back-propagation learning algorithm.…”

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A combined data mining approach using rough set theory and case-based reasoning in medical datasets

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