Expense report processing is a universal pain point. Employees spend 20-30 minutes assembling receipts, typing amounts, and categorizing expenses. Finance teams then spend another 10-15 minutes per report verifying totals and checking policy compliance. For a company with 200 employees submitting monthly reports, that is hundreds of hours per month on a task that adds zero strategic value.

The Problem

Receipts arrive as photos, PDFs, email confirmations, and credit card statements. Each has a different layout. Employees mistype amounts, forget receipts, and miscategorize expenses. Finance teams catch some errors but not all, leading to policy violations and audit findings.

The AI Approach

Document AI extracts merchant name, date, amount, tax, and payment method from receipt images. An LLM then categorizes each expense against your company policy (meals, travel, office supplies) and flags anything that exceeds per-diem limits or requires additional approval.

Three-Step Build

Step 1 - Receipt extraction. Use Amazon Textract or a similar OCR service to extract text and key-value pairs from receipt images. Most receipts yield merchant, date, and total with high confidence.

Step 2 - Categorize and validate. Pass extracted data to an LLM with your expense policy as context. The model categorizes each expense, checks per-diem limits, and flags duplicates (same merchant, same amount, same date).

Step 3 - Auto-populate and route. Generate a pre-filled expense report for employee review. Route reports under threshold amounts for auto-approval; flag others for manager review.

Where It Breaks

Faded thermal receipts and handwritten totals reduce OCR accuracy. Foreign currency receipts need exchange rate lookup. Itemized meal receipts where only some items are business-related require human judgment.

The Production Path

Add a confidence threshold that routes low-confidence extractions to a review queue. Integrate with your ERP or expense management system via API. Most teams achieve 70-80% straight-through processing within the first month.