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Comparing the Performance of Different Data Mining Techniques in Evaluating Loan Applications

Author

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  • Arash Riasi
  • Deshen Wang

Abstract

This study compares the performance of various data mining classifiers in order to find out which classifiers should be used for predicting whether a loan application will be approved or rejected. The study also tries to find the data mining classifiers which have the best performance in predicting whether an approved loan applicant will eventually default on his/her loan or not. The study was performed using a sample of 971 loan applicants. The results indicated that the best data mining classifier for predicting whether a loan applicant will be approved or rejected is LAD Tree, followed by Rotation Forest, Logit Boost, Random Forest, and AD Tree. It was also found that the best classifier for predicting whether an approved applicant will default on his/her loan is Bagging, followed by Simple Cart, J 48, J 48 graft, END, Class Balance ND, Data Near Balance ND, ND, and Ordinal Class Classifier.

Suggested Citation

  • Arash Riasi & Deshen Wang, 2016. "Comparing the Performance of Different Data Mining Techniques in Evaluating Loan Applications," International Business Research, Canadian Center of Science and Education, vol. 9(7), pages 164-187, July.
    • Handle: RePEc:ibn:ibrjnl:v:9:y:2016:i:7:p:164-187
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    References listed on IDEAS

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    Cited by:

    1. Wenzhong Fan, 2016. "Study on Stock Selection Strategy Based on SPSS Regression Mean Quantization," Business and Management Research, Business and Management Research, Sciedu Press, vol. 5(3), pages 26-29, September.

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    More about this item

    Keywords

    data mining; classification; performance of classifiers; loan application; default prediction; loan approval;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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