Data-Driven Billing Reconciliation and Workforce Analytics via UiPath and Power BI

Abstract: This research paper presented an Data driven powered robot for OCR driven billing reconciliation using UiPath. The study used an open source Accounts Receivable dataset exported from an Baan Application system, where customer balances were recalculated from invoices, payments, credits, and adjustments to identify integration errors. The project used three scripts for synthetic data generation, rule based reconciliation, and anomaly detection through Linear Regression. The UiPath based automation was developed with Outlook, Excel, System, and DataTable activities, along with reusable workflows for email reading, OCR extraction, file validation, data cleaning, matching, exception handling, and output generation. Extracted information from invoices and receipts was converted to structured Excel output to be reviewed and reported on. The results showed significant changes in operations including 87.57% less time spent processing invoices, 65% less time spent preparing orders, 66.67% less time spent reconciling inventory payments, 90% less time spent making mistakes when matching invoices and 20 to 25% less money spent on operations.

Keywords: AI ML, OCR, UiPath, Billing Reconciliation, Robotic Process Automation (RPA), Invoice Processing, Employee Analysis.