Intelligent financial data mining and prediction model in economic management decision making

In the context of an increasingly market-oriented and internationalized economic environment, enterprises are facing increasingly severe financial risks. Data mining (DM) technology, as an efficient information processing method, can conduct in-depth analysis of financial data from multiple dimensions and provide more accurate decision support for enterprises. This paper proposes an intelligent financial DM and prediction model based on deep learning (DL) to address the problems of low efficiency, high consumption of human and time resources, and insufficient decision-making intelligence in current enterprise financial management systems (FMS). The model first uses DM technology to preprocess the raw financial data, remove invalid information, retain valuable data, and standardize the data, thereby significantly reducing the consumption of manpower and time resources. Subsequently, the DL algorithm is used to identify potential financial risks and provide timely risk warnings and response strategies for decision-makers. The results indicate that compared to traditional models, the model proposed in this paper has higher accuracy in predicting financial risks in enterprises, and can effectively improve the intelligence level and decision-making efficiency of enterprise financial management.