Abstract
In order to solve the financial risk problem of small and medium-sized logistics enterprises, a log algorithm is used to measure the financial risk of logistics. Combined with the actual situation of logistics finance, through the analysis of randomly selected logistics finance data, quantitatively extract important financial risk indicators, build a credit rating model based on decision tree C4.5 algorithm, and conduct empirical research on the model. The empirical results show that the quantitative financial risk indicator extraction method combined with decision tree C4.5 algorithm credit rating model has high accuracy in the evaluation of logistics finance risk. Therefore, based on the existing logistics financial risk assessment methods and models, the decision tree analysis technology is applied to analyze and make decisions on the credit status of credit applicants, and select companies with good credit, so as to reduce the financial risk of logistics enterprises and improve the efficiency of financial risk management.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call