AI Invoice OCR & Billing Discrepancy Detection
Applied AI-based OCR and analysis to vendor invoices, identifying ~$9K in billing discrepancies and improving financial oversight.
Domain
Technologies & Tools
📊 Impact: ~$9K recovered from billing discrepancies
Problem
Tech Goes Home processes invoices from multiple vendors for devices and services, but invoice validation was largely manual and time-intensive, making it difficult to reliably detect billing errors at scale. Small discrepancies could easily go unnoticed, accumulating into significant financial loss. Invoice review relied on manual visual checks, line-item discrepancies were difficult to catch consistently, no scalable mechanism existed for cross-invoice validation, and financial leakage risk increased as invoice volume grew.
My Role
I owned the solution end-to-end, including designing the AI-based invoice analysis approach, implementing OCR and structured data extraction, normalizing invoice data for comparison, defining discrepancy detection logic, and validating findings and quantifying financial impact. This was a value-driven analytics initiative, not an experiment.
Solution
Designed an AI-powered invoice analysis workflow using OpenAI's OCR and language understanding capabilities to automatically extract, normalize, and analyze invoice data. This system surfaced 10 billing discrepancies totaling approximately $9,000, directly recovering funds and improving financial oversight.
Architecture
High-Level Data Flow
High-Level Data Flow: (1) Invoice documents ingested (PDFs/scans)
OpenAI OCR extracts text and structured fields
Line items and totals normalized
AI-assisted logic compares billed amounts against expected values
Discrepancies flagged for review
Findings validated and reported. Used OCR + language models to handle inconsistent invoice formats, avoiding rigid template-based parsing. Each discrepancy included traceable invoice references enabling finance teams to confidently act on findings.
Key Design Decisions
Results
- ✓Identified 10 billing discrepancies totaling ~$9,000
- ✓Improved confidence in vendor billing accuracy
- ✓Reduced time spent on manual invoice checks
- ✓Demonstrated immediate ROI from applied AI
- ✓Established repeatable model for future financial audits