Design of Enterprise Financial and Economic Risk Early Warning System Based on Machine Learning Algorithms

The design of enterprise financial and economic risk (ER) warning systems based on machine learning algorithms has important background significance. In the current fierce market competition and complex financial environment, enterprises are facing unprecedented financial and ER. Traditional early warning systems often have problems such as low timeliness, missed reports, and false positives. Therefore, introducing machine learning algorithms can more accurately predict and identify potential financial risks, and help enterprises take timely measures to reduce risks. Taking the collected financial data of a manufacturing enterprise as an example, a financial and ER warning model was constructed through the training and validation of machine learning algorithms. In the past five years, the company's operating revenue fluctuated between 9 million to 13 million yuan, and profits fluctuated between 400000 to 700000 yuan; the total assets increased from 18 million yuan to 24 million yuan, and the total liabilities increased from 10 million yuan to 14 million yuan. The designed warning system is based on real-time monitoring and historical data analysis, which can detect potential financial and ER as early as possible in the event of abnormal changes in financial indicators. By establishing a feedback mechanism, the system continuously optimizes accuracy and reliability. This system design provides enterprises with a more accurate and timely financial and ER warning mechanism, which helps to maintain the sustainable development and stable operation of the enterprise.