Dynamic Audit of Internet Finance Based on Machine Learning Algorithm

Abstract

Internet finance is a direction for the development of the new system. The existing research mostly analyzes how to build a risk early warning system from a qualitative perspective to promote a more healthy development of Internet finance companies. This paper aims to study the role of machine learning algorithm in the dynamic audit of Internet finance. It proposes Apriori algorithm, data mining, Bayesian network, and other methods, and related experiments are also conducted on the dynamic audit of Internet financial risk based on machine learning algorithm. The experimental results show that in Internet finance, private enterprises have the highest profit rate, which can reach 20%. But the higher the profit, the greater the financial risk. The machine learning algorithm can make a good intelligent identification of the capital circulation of Internet financial enterprises. This can provide timely feedback on abnormal capital problems, which can help auditors better manage the company’s capital circulation.