Unveiling AI Efficiency: Loan Application Process Optimization Using PM4PY Tool

These days, financial institutions strive to streamline their loan processes for cost reduction, improved customer satisfaction, and enhanced overall efficiency. Process Mining (PM) offers a data-driven approach that identifies bottlenecks, delays, and unnecessary steps within the loan process. By leveraging event logs from these financial institution processes, PM facilitates optimization and automation, resulting in faster loan approvals. Analyzing event logs can create a comprehensive process model representing the institution’s workflow. This study aims to create a process model specifically tailored for the loan application domain by utilizing the advantages offered by the discovery and conformance steps of PM. The algorithms associated with the discovery and conformance steps are analyzed using two datasets related to the loan application process to identify the most suitable model for the loan application process Optimization. The analysis demonstrates the significance of discovery and conformance algorithms for different quality matrices while generating an effective process model. The proposed methodology reveals that each discovery algorithm comes with its own set of advantages and disadvantages, characterized by varying values of quality metrics. Consequently, the selection of a discovery algorithm is based on the specific quality criteria needed for the task at hand.