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Artificial immune algorithm-based credit evaluation for mobile telephone customers

Author

Listed:
  • Yang Zong-Chang

    (Hunan University of Science and Technology, Xiangtan, China)

  • Kuang Hong

    (Hunan University of Science and Technology, Xiangtan, China)

  • Xu Ji-sheng

    (Wuhan University, Wuhan, China)

  • Sun Hong

    (Wuhan University, Wuhan, China)

Abstract

The arrearage problem is a critical concern for China’s mobile communication services industry. Analysis of customer credit evaluation provides this study with a potential viable solution to the arrearage problem in China. By employing an artificial immune algorithm (AIA), a measure of customer credit based on customer attributes is proposed. This method was applied to one China mobile communication services company with approximately 400 000 customers yielding satisfying results. Utilizing traditional predictive accuracy and alternative metrics, performance comparisons of the proposed AIA were made using the feed-forward back propagation artificial neural network and the logistic regression model. A decision tree analysis of anticipated benefits was performed and indicates workability of the proposed method based on customer credit evaluation.

Suggested Citation

  • Yang Zong-Chang & Kuang Hong & Xu Ji-sheng & Sun Hong, 2015. "Artificial immune algorithm-based credit evaluation for mobile telephone customers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(9), pages 1533-1541, September.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:9:p:1533-1541
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    Cited by:

    1. Xing, Jin & Chi, Guotai & Pan, Ancheng, 2024. "Instance-dependent misclassification cost-sensitive learning for default prediction," Research in International Business and Finance, Elsevier, vol. 69(C).
    2. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.

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