Big Data-Driven Efficiency Optimization in Enterprise Financial Shared Services

This study constructs a big data-driven process optimization model for a financial sharing center. It addresses the research gap regarding the empirical analysis of real process log data in integrating big data capabilities with organizational efficiency. The methodology involves analyzing 24 months of event logs from a manufacturing group using process mining, state machine modeling, and fixed-effect regression to evaluate operational efficiency. Results indicate that big data empowerment significantly shortens node waiting times, reducing rule pre-check delays by 32.5%, and improves first-pass yields. Findings also reveal that process complexity and analytics intensity influence efficiency gains, with high-frequency manual processes showing the greatest improvement. These results imply that a data-driven closed-loop system is essential for enhancing organizational effectiveness, intelligent financial management, and sustainable digital transformation.