Risk Assessment of Operator’s Big Data Internet of Things Credit Financial Management Based on Machine Learning

Abstract

Credit risk evaluation innovation is of incredible importance to monetary establishments. AI innovation can fundamentally work on the precision and versatility of credit risk evaluation. This paper aims to study the risk assessment of operator big data Internet of Things credit financial management based on machine learning. It proposes machine learning-related algorithms, including the introduction of logistic model and decision tree model, as well as related concepts of credit financial management risk. This paper proposes that big data can be better used to reduce financial risk management problems and proposes specific actions based on the actual situation of the company. This paper selects company A for financial risk management evaluation through case analysis and compares it with three major e-commerce companies. The experimental results show that the earnings per share of company A is between −0.99 and 0. Company A is still in a state of loss in recent years, and there are certain debt risks, operational risks, and capital risks.