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.
Entity_Client
FinCore Ltd.
Primary_Role
Automation Engineer
Duration_Log
2 months
Resource_Team
2 developers
Project_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.
Operational_Process
Built RPA bots integrated with UiPath, implemented OCR for invoice scanning, created validation rules, and integrated with accounting software (QuickBooks) for automatic entry.
Core_Capabilities
Performance_Metrics
Processing Time
DATA_POINT: 80% reduction
Automation Rate
DATA_POINT: of invoices
Cost Per Invoice
DATA_POINT: 84% lower
Payment Cycle
DATA_POINT: time
Error Rate
DATA_POINT: reduction
Annual Savings
DATA_POINT: labor
Conflict_Resolution
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.