Neural Networks in Business Forecasting
Neural Networks in Business Forecasting
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Neural nets versus conventional techniques in credit scoring in Egyptian banking
Expert Systems with Applications: An International Journal
A practical approach to credit scoring
Expert Systems with Applications: An International Journal
Support vector machines for credit scoring and discovery of significant features
Expert Systems with Applications: An International Journal
Constructing a reassigning credit scoring model
Expert Systems with Applications: An International Journal
Building credit scoring models using genetic programming
Expert Systems with Applications: An International Journal
Granular computing: past, present, and future
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The credit card industry has been growing rapidly and thus huge numbers of consumers' credit data are collected by the credit department of the banks. The credit scoring managers often evaluate the consumer's credit with intuitive experience. However, with the support of the credit classification models, the managers can accurately evaluate the applicants' credit score. In this study, a neurocomputing-based granular approach is proposed to model credit scoring. Granular computing is used to compute the size of training and testing groups. Artificial neural networks (ANN) and data envelopment analysis (DEA) are used to model credit lending decisions in the online and offline manner, respectively. Proposed method is composed of three distinct stages based on trust and credibility concept. Trust is introduced and modeled via ANN in online module. Also credibility is modeled via DEA in offline module in present study. This paper is a pioneer in examining the concept of granularity for selecting the optimum size of testing and training group in machine learning area. In addition, proposing flexible trust ranges comparing to the current constant ones will support the importance of customers with higher credit scores to financial markets. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card data set obtained from the UCI repository.