A Lightweight Robotic Process Automation Framework for Financial Analytics in Spreadsheet-Centric SMEs

Small and medium-sized enterprises (SMEs) frequently depend on spreadsheet-based financial reporting due to limited budgets and constrained access to enterprise analytics systems. As transaction volumes increase, manual profit and loss computation becomes time-intensive and prone to inconsistencies. This study proposes and evaluates a modular robotic process automation (RPA) framework designed to enhance spreadsheet-centric financial analytics without requiring enterprise system replacement. The framework is implemented as a unified pipeline using UiPath. Statistical anomaly detection mechanisms are integrated to identify abnormal revenue deviations and expense spikes in operational data. Experimental benchmarking compares manual spreadsheet processing with automated workflow execution using execution time, error exposure, reporting latency, and scalability as evaluation criteria. Empirical evaluation across five datasets spanning 300 to 3000 transactions demonstrates time reductions of 88.6% to 95.5% and error reductions of 93.3% to 95.5% relative to manual spreadsheet processing. Scalability analysis confirms linear growth of automated runtime with transaction volume, in contrast to the superlinear growth observed in manual processing. A cost feasibility analysis further indicates that lightweight RPA can significantly reduce operational costs in SME environments up to 88.6%. The study contributes a structured automation architecture that integrates spreadsheet automation with statistical monitoring to support financial oversight and decision support. The findings suggest that interface-level automation provides a viable transitional pathway for SMEs seeking incremental digital transformation while preserving existing spreadsheet infrastructures.