Thus, customer payment behavior is a part of data processing, and the payment behavior rules are used to produce derivative attributes.
Customer payment behavior can then be directly stored in the data.
Next, a clustering algorithm is used for customer segmentation, and the cluster of customers who paid their fees punctually is eliminated to reduce data imbalances.
Finally, a decision tree is utilized to construct a prediction model from the rest of the data by using the derivative attributes from the association rules and the attributes provided by the telecom providers.
Thus,based on the real dataset given by the telecom providers and the proposed framework, service personnel can remind customers who may make delayed payments to pay the fee.