AP Invoice Automation for a Multi-Company Finance Operation
The project was designed to automate the complete Accounts Payable invoice lifecycle across more than seven operating companies by replacing manual, email- and paper-based processes with a fully digital workflow. Using OCR and intelligent automation, invoices are automatically captured, validated, approved, and posted directly into the ERP system, significantly reducing manual effort, improving data accuracy, and accelerating overall invoice processing time.
Client Overview
The client is a diversified business group managing multiple companies under a centralised finance team. The organisation handles a large number of vendor invoices every day. They were looking for a solution that could simplify invoice processing, reduce dependency on manual work, improve turnaround time, and ensure better financial control across all companies.
Technical Stack
Industry
Investment Management
Region
UAE (Sultanate-wide implementation)
Project-size
Non-Disclosable
Company size
Large Scale Authority (National Government Body)
Implementation Highlights
End-to-end AP automation: Automated the complete invoice lifecycle from email ingestion and OCR processing to approvals and direct posting in Oracle ERP.
Intelligent document processing: Implemented ABBYY-based OCR with human-in-the-loop verification to ensure high accuracy across varied invoice formats.
Integrated approval and validation workflows: Auto-generated SharePoint tickets with real-time status tracking, PO/GRN matching, and automated accounting creation.
Robust exception and communication handling: Enabled automated email acknowledgements, clear exception workflows, and user notifications for seamless processing and control.
Challenges & Solutions
Processing high volumes of unstructured invoice formats across multiple vendors.
Solution: Implemented ABBYY FlexiCapture with custom extraction templates and classification rules. This enabled automatic detection of different vendor layouts and accurate field extraction. Manual data entry has been reduced significantly, and overall data accuracy has improved.
Reducing manual verification effort caused by low-confidence OCR fields.
Solution: Integrated ABBYY Verification Station with UiPath so only low-confidence fields are sent for human review. Once verified, the clean data is exported directly to Excel. This ensures high data quality while minimizing unnecessary manual effort.
Preventing ERP posting failures resulting from incomplete or missing invoice fields.
Solution: Introduced mandatory field validation and error tagging in SharePoint before ERP posting. Automatic notifications alert users when data is incomplete or incorrect. This reduced ERP posting failures and rework effort.
Standardising how exceptions are handled when matching PO and Receipt data.
Solution: Defined standardised business rules for PO/Receipt mismatches and implemented an escalation workflow. Unresolved exceptions are routed to the right stakeholders with clear SLAs. This made exception handling faster, consistent, and auditable.
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Results & Impact
- Achieved a 70–80% reduction in manual effort, significantly decreasing the workload on finance and operations teams.
- Ensured 100% accuracy in data entry for validated invoices, eliminating errors caused by manual processing.
- Enabled real-time visibility of invoice status through SharePoint, improving transparency and tracking across the workflow.
- Delivered faster invoice processing times — reducing cycle time from hours to just minutes per invoice.
Key Learnings & Next Steps
- OCR performance is highly dependent on invoice quality and supplier formats, so template design and sample coverage are critical for success.
- Standardising SharePoint fields significantly reduced ERP posting exceptions, resulting in cleaner and more reliable downstream data.
- Robust exception-handling processes are crucial for ERP automation, as even minor data gaps can disrupt end-to-end automation.
- Human-in-the-loop verification provides a balance between accuracy and speed, ensuring data quality without slowing the overall process.
