Research Article Credit Rating Using Type-2 Fuzzy Neural Networks Rahib H. Abiyev Department of Computer Engineering, Near East University, P.O. Box 670, Lefosa, TRNC, Mersin 10, Turkey Correspondence should be addressed to Rahib H. Abiyev; rahib@neu.edu.tr Received 6 December 2013; Accepted 15 February 2014; Published 27 March 2014 Academic Editor: Her-Terng Yau Copyright © 2014 Rahib H. Abiyev. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Nowadays various new technologies such as artifcial neural networks, genetic algorithms, and decision trees are used for modelling of credit rating. Tis paper presents design of credit rating model using a type-2 fuzzy neural networks (FNN). In the paper, the structure of the type-2 FNN is designed and its learning algorithm is derived. Te proposed network is constructed on the base of a set of fuzzy rules that includes type-2 fuzzy sets in the antecedent part and a linear function in the consequent part of the rules. A fuzzy clustering algorithm and gradient learning algorithm are implemented for generation of the rules and identifcation of parameters. Efectiveness of the proposed system is evaluated with the results obtained from the simulation of type-2 FNN based systems and with the comparative simulation results of previous related models. 1. Introduction Credit rating is a method of measuring the creditworthiness of potential customers by analyzing their historical bank data and is a very important problem in fnance. Credit rating shows whether a company has a history of fnancial stability and responsible credit management. Te basic factors afect- ing credit ratings are payment history, amounts owed, length of credit history, having new credits, and types of credit. Satisfactory results obtained for these factors determine the creditworthiness of the customers. Te basic aim of credit approval is to avoid huge amount of losses that may be associated with any type of inappropriate decision. Te design of credit rating models allows reducing the cost of credit analysis, reducing possible risk, and enabling faster credit decision. Te solving of these problems will allow us to increase the beneft of bank fnance system. Credit rating is a binary classifcation problem that classifes credit customers into predefned “good” and “bad” groups based on an observation. Numerous credit rating models have been developed in order to evaluate and classify loan customers to a good applicant group or either to a bad applicant group. Te aim of these studies focused on increasing the accuracy rate of credit rating models since even little bit of improvement will result in signifcant cost savings. Credit rating models are based on collecting huge amounts of data about credit customers in order to avoid making the wrong decision. Credit rating models are based on the analysis of their related attributes, such as age, marriage status, and income, or on their past records, and so forth. Recently various kinds of credit rating models have been developed and applied to support credit approval decisions. Tese are traditional models based on statistical analysis such as discriminant analysis, logistic regression, and decision tree [1, 2]. Te statistical models can perform well in many applications, but when the relationships of the system are nonlinear, the accuracy of these models decreases. Other models are based on rough sets, neural networks, genetic algorithm, and support vector machine [3–6]. Te artifcial neural networks (ANN) have the ability of learning nonlinear relationships in a system. Because of dealing with nonlinear patterns, ANN has shown better performance in accuracy in contrast to the traditional statistical methods such as discriminant analysis and logistic regression [3, 7, 8]. In [8, 9] genetic programming (GP) has been used in classifcation [8, 9]. GP is viewed as a tree-based structure and is employed to build the discriminant function for the credit rating problems [8]. Afer initializing the tree, the operators of genetic algorithm (GA) such as crossover, mutation, and reproduction are applied for fnding of optimal generation. Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 460916, 8 pages http://dx.doi.org/10.1155/2014/460916