Financial Data Mining and Analysis Based on Intelligent Algorithms

With the rapid development of big data and artificial intelligence technologies, the scale and complexity of financial data continue to increase, and traditional analysis methods can no longer meet the needs of enterprises for efficient and accurate data mining and analysis. However, current financial data mining still faces problems such as uneven data quality, difficult feature selection, and limited model generalization capabilities, which leads to inaccurate analysis results and makes it difficult to effectively support corporate decision-making. In response to these problems, this paper first outlines the application background of intelligent algorithms in financial data mining and analyzes the shortcomings of current research. Subsequently, this paper introduces intelligent algorithms in detail, preprocesses and selects features for financial data, and then builds and optimizes data mining models. Through experimental verification, the method proposed in this paper has achieved remarkable results in improving the efficiency and accuracy of data mining, especially in tasks such as financial data classification, prediction and anomaly detection. Its detection capability can reach up to 100% with only small fluctuations, which shows that the data mining model can provide enterprises with more reliable decision-making basis.