Intelligent recognition of financial fraud based on CART decision tree
Guiyun Chen · International Journal of Information and Communication Technology · 2025
This paper proposes an intelligent recognition model of financial fraud based on classification and regression tree (CART) decision tree, which aims to improve the recognition rate of financial fraud and provide a preliminary reference for other industries to use non-financial information for fraud recognition. The decision tree model adopted is tuning Iterative Dichotomiser 3 (ID3) algorithm and CART algorithm, and optimises the decision tree parameters by particle swarm to avoid the occurrence of over-fitting. It is found that the area under curve (AUC) of CART tree recognition method is significantly higher than that of random forest (RF) and neural network recognition methods, reaching 70%, which has a good recognition effect. It can be seen that parameter combination search can make the accuracy of CART decision tree model achieve the best effect, and has a positive effect on improving the intelligent recognition effect of financial fraud behaviour.