Financial Process Efficiency through Advanced Analytics: Process Mining, DEA, and Machine Learning

This paper explores the synergies between process mining and Data Envelopment Analysis (DEA) in business process management. Process mining offers insights into organizational workflows by analyzing event logs, while DEA provides a robust framework for efficiency assessment across diverse sectors. In the initial stage, hierarchical data analysis was conducted to construct process information diagrams and categorize process traces based on various factors, including decision points for each case. In the second stage, process analysis concepts and process discovery algorithms were utilized to define and compute key performance indicators (KPIs) from the perspectives of organizational resources and individual cases, followed by constructing process diagrams using Alpha, Inductive, and Heuristic miners. In the third stage, a scenario-based robust DEA model was applied to rank the KPIs of employees derived from the event log data. Furthermore, we examine how the proposed scenario-based Robust DEA revolutionizes efficiency assessment, providing decision-makers with a comprehensive framework for evaluating organizational performance. Ultimately, by integrating ML prediction and behavior analysis, organizations can anticipate future trends, optimize operations, and drive continuous improvement. Ultimately, process mining and DEA represent indispensable tools for organizations seeking to enhance operational efficiency, mitigate risks, and achieve strategic objectives in today's dynamic business environment. The findings demonstrate that this comprehensive integration of techniques provides profound insights into the event logs, identifying strengths, weaknesses, bottlenecks, and overall system efficiency, thereby facilitating organizational improvement and optimization.