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• W2288565500 abstract "One of the issues which must be taken into account by credit policy makers in banking industry is risk management. Credit risk is one of the most serious risks faced by banks. This risk may bring about negative consequences such as customers' inability or unwillingness to fulfill their obligations to the bank. To manage and control credit risk, the use customer credit classification system is inevitable. Such system puts customers in appropriate classes based on available data and records .Credit scoring generally aims to provide an accurate prediction of customer competency. To this end, multiple statistical techniques plus artificial intelligence have been used. However, most models are not able to provide multi-class categorizations using two-class data. Nevertheless, a customer is assessed based on some degrees of goodness or badness. Therefore, after removing data noises through clustering, it is usually attempted to use a multi-class support vector machine (SVM) to classify new data. Feature selection and parameter adjustment is performed via genetic algorithm. Compared with other scoring models, the proposed model has been able to improve classification accuracy for German and Australian credit datasets One of the key success factors in lender organizations in general and banks in particular is to assess the borrower's credit position. Credit risk appears if a wrong decision is made when confirming the borrower's request. Therefore, credit risk is one of the challenges that may be faced by financial institutions. Historically, credit risk was assessed through judgmental and subjective decisions made by creditors through analyzing available data. Therefore, wrong decisions concerning to grant or not to grant loans were taken based on a mainly subjective and time-consuming process (1). As such, credit scoring is a very simple problem of data mining classification. Credit scoring is generally a term used to describe formal statistical methods to classify credit applicants into good and bad groups. In fact, credit scoring aims to divide applicants into two groups: applicants with good credit and those with bad credit (2). In most studies, a number of models are used which instead of providing two-class predictions, divide customers into several classes using specific criteria or suitable cut-off values. Logit and probit models (3) or neural networks (4) are examples of such methods. However, the selection of suitable cut-off values that can work well under trial and test conditions is a very complicated process. Besides, the use of such values with these models, each with its own weaknesses, is problematic. Logit and probit models, for instance, assume that data are normally distributed and the model used will fit out a normal distribution on data. When it comes to neural networks, the adjustment of initial parameter is an important issue that must be determined in advance. Imbalanced credit data can also be regarded as another serious problem in this regard. In the real world, the number of customers who make a default when repaying their obligations is much less than those who repay their debts on time. To eliminate the above problems, the present study seeks to propose a model which is mainly based on clustering mechanism. To improve the model performance and precision, the model parameter and features are selected simultaneously by genetic algorithm to provide a good model for credit scoring of banking customer." @default.
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• W2288565500 date "2016-02-25" @default.
• W2288565500 modified "2023-09-24" @default.
• W2288565500 title "A New Method Based on Clustering and Feature Selection for Credit Scoring of Banking Customers" @default.
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