Customer Segmentation of Personal Credit using Recency, Frequency, Monetary (RFM) and K-means on Financial Industry

This research focuses on how to build a segmentation model for credit customers to identify the potential for defaulting credit customers based on their transaction history. Currently, there is no segmentation available for this possibility of payment failure. Credit scoring helps in minimizing credit risk when applying for credit. However, using RFM (Recency, Frequency, Monetary) models helps to score each transaction variable of the customer's financial activity. K-means then assists in the process of segmenting the results of the RFM model scoring, which occurs in the middle of the customer's repayment schedule. Challenge is how to decide the variable that can be used in RFM models and how to interpret the clusters that have been formed and the actual implementation of the customer. The Bank can divide the clusters that have possibility of payment failure by their customers so that banks can take preventive actions and as information for the collection system to be able to make payment withdrawals or billing.

@article{Rizkyanto2023,
  title     = {Customer Segmentation of Personal Credit using Recency, Frequency, Monetary (RFM) and K-means on Financial Industry},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {4},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Hafidh Rizkyanto and Ford Lumban Gaol},
  doi       = {10.14569/IJACSA.2023.0140417},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140417}
}