Digitat
Finance•RPA Automation•2024

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.

InvoiceBot

Client

FinCore Ltd.

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. InvoiceBot automates the entire process with 87% automation rate.

Process

Built RPA bots integrated with UiPath, implemented OCR for invoice scanning, created validation rules, and integrated with accounting software (QuickBooks) for automatic entry.

Key Features

✓Invoice scanning and OCR with high accuracy
✓Automatic data extraction (vendor, amount, date, PO)
✓PO matching and validation against received goods
✓Tax code assignment based on rules
✓Duplicate invoice detection
✓Automatic journal entry creation in accounting system
✓Error logging and exception handling
✓Audit trail for all processed invoices
✓Dashboard with processing metrics and KPIs
✓User feedback loop for continuous improvement

Challenges & Solutions

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%.

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%.

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%.

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.

Results

Processing Time

40 hours/week→8 hours/week

80% reduction

Automation Rate

0%→94%

of invoices

Cost Per Invoice

$4.20→$0.65

84% lower

Payment Cycle

14 days→3 days

time

Error Rate

12%→0.3%

reduction

Annual Savings

0→$156k

labor

Goals

  • •Automate invoice data entry and processing
  • •Reduce manual effort in accounting department
  • •Improve payment cycle time
  • •Eliminate data entry errors

Tech Stack

  • •Python
  • •UiPath
  • •PostgreSQL

Target Users

  • •Accounting teams
  • •Finance managers
  • •Accounts payable staff

Key Learnings

  • •OCR accuracy is critical—invest in preprocessing and validation
  • •Fuzzy matching with thresholds is better than exact matching in real-world scenarios
  • •Human review queues for edge cases are essential for trust
  • •Legacy system integrations require creative workarounds

Future Plans

  • •Add three-way matching (PO, invoice, receipt)
  • •Implement expense categorization with AI
  • •Add supplier performance analytics
  • •Expand to payment processing automation