Exploration of association rule mining between lost-linking features and modes of loan customers using the FP-growth algorithm for risk warning strategies

Abstract

In the new model of China’s dual-circulation economy, the opening-up and deepening of financial markets have imposed higher requirements on the risk management capacity of financial institutions, with the issue of loan customers losing contact and defaulting becoming an urgent concern. Based on desensitized samples of lost-linking customers (with multidimensional features such as communication behavior and loan qualifications), this study uses the FP-Growth algorithm to systematically mine association rules between loss-of-contact features and three modes: “Hide and Seek”, “Flee with the Money”, and “False Disappearance”, providing effective risk management strategies for financial institutions. Through association rule mining, this study reveals significant correlations between some feature combinations and lost-linking modes. The results reveal substantial variations in correlation strength among different feature combinations and lost-linking modes, and the association strength increases significantly with the prolongation of overdue time. The results provide banks with quantitative early warning signs based on feature combinations, which can be applied to risk-grading monitoring systems. The research emphasizes the requirement for combined analysis of multidimensional features and dynamic monitoring in precise risk control.