InvoiceBot
An RPA system automating invoice processing, data extraction, and accounting entries with OCR and validation, reducing manual data entry by 80% with audit trails.
- Client
- FinCore Ltd.
- Industry
- Finance
- Type
- RPA Automation
- Year
- 2024
- Our role
- Automation Engineer
- Timeline
- 2 months
- Team
- 2 developers
Overview
FinCore's accounting team spent 40+ hours/week manually processing invoices: extracting data, validating against POs, entering into accounting system, and reconciling.
Process.
- Built RPA bots integrated with UiPath, implemented OCR for invoice scanning, created validation rules, and integrated with accounting software (QuickBooks) for automatic entry.
What we built.
- featInvoice scanning and OCR with high accuracy
- featAutomatic data extraction (vendor, amount, date, PO)
- featPO matching and validation against received goods
- featTax code assignment based on rules
- featDuplicate invoice detection
- featAutomatic journal entry creation in accounting system
Hard parts.
fixInconsistent invoice formats from different vendors
Implemented multi-format OCR with preprocessing, used rule-based parsing for common formats, added machine learning classifier to detect invoice type, and implemented fallback to manual review for edge cases. Accuracy improved to 94%.
fixPO matching failures causing delayed payments
Implemented fuzzy matching algorithm with threshold tuning, added vendor normalization, created amount tolerance rules (±2%), and added human review queue for unmatched invoices. Matching rate improved to 94%.
fixTax calculations and compliance errors
Created detailed tax rule library, added multi-factor decision logic (vendor type, item category, geography), implemented validation against tax databases, and created quarterly compliance reviews. Accuracy improved to 99.2%.
fixIntegration with legacy accounting system (QuickBooks)
Built custom integration layer that could work around API limitations, implemented transaction buffering and retry logic, and created data validation before entry. 99.8% successful entries achieved.
Built with
Python UiPath PostgreSQL
Building something like this?
Tell us where the product is today. We reply within 12 hours.