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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92543

    Title: A Comparative Study of Data Mining Techniques for Credit Scoring in Banking
    Authors: Huang, Shih-Chen;Day, Min-Yuh
    Contributors: 淡江大學資訊管理學系
    Keywords: Classification Method;Credit Risk Score;Data Mining;SAS Enterprise Miner;Support Vector Machine (SVM)
    Date: 2013-08-14
    Issue Date: 2013-10-18 05:40:42 (UTC+8)
    Publisher: IEEE Press
    Abstract: Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.
    Relation: Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), pp.684-691
    Appears in Collections:[Graduate Institute & Department of Information Management] Proceeding

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    The Analysis Approach of Voice of the Customers and Critical to Quality_英文摘要.docx摘要14KbMicrosoft Word119View/Open

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