Analysis of late payment of credit card based on data mining technology

With the rapid development of the financial sector, the banking card business has seen a year-on-year increase in adverse overdue risks due to the long-term absence of a preemptive warning system for overdue risks. Consequently, this paper employs a decision tree model to mine and analyze credit card holder data provided by the Kaggle dataset website’s GiveMeSomeCredit project. Subsequently, based on the findings from the decision tree analysis, an evaluation matrix is constructed using the Analytic Hierarchy Process (AHP) to develop a credit scoring model. The aim is to determine the likelihood of overdue repayments by examining users’ repayment records and personal basic information.