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Using data mining and neural networks techniques to propose a new hybrid customer behaviour analysis and credit scoring model in banking services based on a developed RFM analysis method

Author

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  • Mahmood Alborzi
  • Mohammad Khanbabaei

Abstract

Nowadays, credit scoring is one of the major activities in banks and other financial institutions. Also, banks need to identify customers' behaviour to segment and classify valuable customers. Data mining techniques and RFM analysis method can help banks develop customer behaviour analysis and credit scoring systems. Many researchers have deployed credit scoring and RFM analysis method in their studies, separately. In this paper, a new hybrid model of behavioural scoring and credit scoring based on data mining and neural networks techniques is presented for the field of banking. In this hybrid model, a new enhanced WRFMLCs analysis method is developed using clustering and classification techniques. The results demonstrate that the proposed model can be deployed to effectively segment and classify valuable bank customers.

Suggested Citation

  • Mahmood Alborzi & Mohammad Khanbabaei, 2016. "Using data mining and neural networks techniques to propose a new hybrid customer behaviour analysis and credit scoring model in banking services based on a developed RFM analysis method," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 23(1), pages 1-22.
    • Handle: RePEc:ids:ijbisy:v:23:y:2016:i:1:p:1-22
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    Citations

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    Cited by:

    1. Omar H. Fares & Irfan Butt & Seung Hwan Mark Lee, 2023. "Utilization of artificial intelligence in the banking sector: a systematic literature review," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 835-852, December.
    2. Jie Sun & Jie Li & Hamido Fujita & Wenguo Ai, 2023. "Multiclass financial distress prediction based on one‐versus‐one decomposition integrated with improved decision‐directed acyclic graph," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1167-1186, August.
    3. Yingqiu Zhu & Qiong Deng & Danyang Huang & Bingyi Jing & Bo Zhang, 2021. "Clustering based on Kolmogorov–Smirnov statistic with application to bank card transaction data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 558-578, June.

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