Our accounting and finance team was drowning in a massive influx of invoices that all required manual processing. We were dealing with thousands of invoices every month, manually matching them against purchase orders and goods receipts, and the error rate was creeping up while throughput was getting worse. The real breakthrough came when we combined process mining with RPA instead of just throwing automation at the problem blindly.
We started by running process mining on our invoice-to-pay logs to understand where the actual bottlenecks were and what variations existed in how invoices flowed through the system. The mining revealed that a good chunk of invoices could go straight through automated three-way matching if we built the integration properly, while others needed human review for specific exception types. Based on that, we deployed an RPA solution that integrated directly with our ERP back-end for automatic matching between invoice, PO, and goods receipt.
The results were significant: we cut human labor on routine invoices by around seventy percent, error rates dropped from about twenty percent down to five percent, and the finance team could finally focus on exceptions and strategic planning instead of data entry. The key was using process mining first to map the real flow and identify which steps were truly automatable, then designing the RPA to handle those specific patterns. We kept human-in-the-loop for edge cases, which gave us both efficiency and quality assurance.