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Using data mining to integrate recency-frequency-monetary value analysis and credit scoring methods for bank customer behaviour analysis

Author

Listed:
  • Mohammad Khanbabaei
  • Pantea Parsi
  • Najmeh Farhadi

Abstract

Banks apply credit scoring to identify customers with low credit risk. Additionally, recency-frequency-monetary value (RFM) analysis method is suitable for identifying valuable bank customers. Data mining techniques can be used to discover useful patterns hidden in customer data. However, in previous research, data mining has been used separately in both credit scoring and RFM approaches. To evaluate customer behaviour, banks must employ credit scoring and RFM analysis method, simultaneously. This study proposes a framework for using data mining techniques to integrate credit scoring and RFM methods in the field of banking. In this framework, k-means had better performance than Kohonen network and DBSCAN to identify and cluster valuable customers based on the RFM and credit scoring indices. Moreover, the C5 decision tree, BN, and SVM with 94.10%, 92.71%, and 92.36% accuracy had better performance to classify valuable bank customers based on RFM and credit scoring indices.

Suggested Citation

  • Mohammad Khanbabaei & Pantea Parsi & Najmeh Farhadi, 2023. "Using data mining to integrate recency-frequency-monetary value analysis and credit scoring methods for bank customer behaviour analysis," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 15(4), pages 369-392.
    • Handle: RePEc:ids:ijdmmm:v:15:y:2023:i:4:p:369-392
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