expert systems can help to build banks customers' credit scoring models. here, selection of key features of the credit scoring is important. also, it is possible to express the features values as fuzzy. the problem is how to improve features selection by genetic algorithm, in way that these features can be employed as input in fuzzy expert system. this paper presents a hybrid credit scorin...

decision trees as one of the data mining techniques, is used in credit scoring of bank customers. the main problem is the construction of decision trees in that they can classify customers optimally. this paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. genetic algorithm can help in credit sco...

credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. for this sake, in this paper a data envelopment analysis- discriminant analysis (dea-da) approach is us...

This paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in Credit scoring. Yet there isn’t any literature in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct onli...

this paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in credit scoring. yet there isn’t any literature in the field of data mining applications in credit scoring. using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the science direct onli...

Credit scoring has gained more and more attentions both in academic world and the business community today. Many modeling techniques have been developed to tackle the credit scoring tasks. Credit scoring models have been widely used by financial institutions to determine if loan customers belong to either a good applicant group or a bad applicant group. The advantages of using credit scoring mo...

Competition of the consumer credit market in Taiwan has become severe recently. Therefore, most financial institutions actively develop credit scoring models based on assessments of the credit approval of new customers and the credit risk management of existing customers. This study uses a genetic algorithm for feature selection and decision trees for customer segmentation. Moreover, it utilize...

Credit scoring is an important tool in financial institutions, which can be used in credit granting decision. Credit applications are marked by credit scoring models and those with high marks will be treated as “good”, while those with low marks will be regarded as “bad”. As data mining technique develops, automatic credit scoring systems are warmly welcomed for their high efficiency and object...

The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant’s credit score accordin...

Credit scoring model is an important tool for assessing risks in financial industry, consequently the majority of financial institutions actively develops credit scoring model on the credit approval assessment of new customers and the credit risk management of existing customers. Nonetheless, most past researches used the one-dimensional credit scoring model to measure customer risk. In this st...