In the context of global financial digital transformation, RPA has become a core technological tool for enhancing the efficiency of financial shared service centers. The distribution industry features a high degree of document standardization, making it naturally well-suited for RPA applications. However, small- and medium-sized circulation enterprises generally face practical challenges such as difficulty in quantifying implementation results and inefficient human-machine collaboration. This paper takes accounts payable and expense reimbursement-two key processes-as its research objects, constructing a three-dimensional quantitative evaluation system encompassing “efficiency, error rate, and cost.” By employing paired-sample tests and multi-case empirical methods, we measure the application effects at specific process nodes and propose a lightweight human-machine collaboration solution tailored to small- and medium-sized FSSCs. The study shows that RPA can boost process handling efficiency by more than 70%, reduce standardized operation error rates by around 90%, and cut direct labor costs by over 35%. Clearly defining the boundaries of human and machine responsibilities and establishing closed-loop integration mechanisms are essential prerequisites for fully unlocking the value of this technology. This research fills a gap in the quantitative study of RPA applications in specific financial processes within the distribution industry, providing reusable theoretical and practical references for small- and medium-sized circulation enterprises seeking to promote financial digital transformation at low cost.
Financial Shared Service Center; Robotic Process Automation (RPA); Distribution Industry; Quantitative Assessment; Human-Machine Collaboration.
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