Abstract
Nowadays various new technologies such as artificial neural networks, genetic algorithms, and decision trees are used for modelling of credit rating. This 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. The 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 identification of parameters. Effectiveness 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.
Highlights
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 finance
The comparisons of the type-2 fuzzy neural networks (FNN) model with the models based on support vector machine (SVM), neural networks (NNs), and type-1 FNN will be made
The result of the simulation of the type-2 FNN prediction model is compared with results of simulations of the support vector machine (SVM), neural networks (NNs), and FNN based classification models
Summary
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 finance. Various kinds of credit rating models have been developed and applied to support credit approval decisions These are traditional models based on statistical analysis such as discriminant analysis, logistic regression, and decision tree [1, 2]. Many times the attributes of the credit customers are characterised by uncertainty and fuzziness of information For these conditions the previous models all have certain drawbacks and never achieve stability and accuracy simultaneously. The combination of fuzzy systems and support vector machine or neural networks (NNs) successfully has been used for credit rating purposes for increasing the accuracy of the designed model [10,11,12,13]. In this paper the combination of the type-2 fuzzy systems and neural networks is considered for credit rating.
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