Application and optimization of data mining technology in financial sharing service center

This paper focuses on the application and optimization of data mining technology in Financial Shared Service Center (FSSC), aiming at solving the shortcomings of traditional FSSC in data utilization, process efficiency and risk management and control through the deep integration of technology and management practice. This paper studies and constructs a multidimensional data governance system, integrates multi-source heterogeneous data through Knowledge Graph (KG), and introduces federated learning and privacy protection technologies to improve data quality and security. In the aspect of algorithm adaptation, a dynamic cash flow forecasting system based on Prophet model and a risk early warning scheme based on two-tier architecture are designed, and the approval process is optimized by combining process mining and reinforcement learning (RL). In addition, cross-functional agile collaboration mechanism and dynamic optimization closed-loop system are proposed to improve organizational collaboration efficiency and model adaptability. The empirical study takes multinational manufacturing group as an example, and uses the improved Prophet-ARIMA mixed model to forecast. The results show that the data quality is significantly improved, the forecasting accuracy and stability are better than the traditional methods, the decision response time is significantly improved, and the anomaly detection accuracy is high, which verifies the application value and optimization effect of data mining technology in FSSC.