The Prediction Trend of Enterprise Financial Risk based on Machine Learning ARIMA Model - Dong, Xinqi; Dang, Bo; Zang, Hengyi; Li, Shaojie; Ma, Danqing (Journal of Theory and Practice of Engineering Science, 2024)
The relevance of AI technology to abnormal prediction of corporate financial statements is reflected in its ability to identify and predict abnormal patterns in financial data through advanced algorithms. This predictive power is primarily based on machine learning and data mining techniques such as decision trees, neural networks, and deep learning. These techniques can analyze historical financial data and learn patterns and trends to effectively predict possible future anomalies, such as fraud, errors, or other irregularities. By identifying these anomalies in a timely manner, businesses can take preventive measures to reduce potential financial losses. The role of AI in the financial management of enterprises is reflected in its ability to process and analyze large amounts of complex data. AI technology can help companies automate cumbersome financial processes such as invoice processing and reimbursement management, increasing efficiency and accuracy. Based on AI deep learning algorithm, this paper uses ARIMA regression model to predict financial anomalies and financial development trends of enterprises, so as to help enterprises manage risk and make investment decisions, and better cope with financial risks and grasp investment opportunities.