Invoice Risk Detection
Built on the Tire Extraction platform. AI reads invoices from images and PDFs, cross-checks the data, then scores risk and flags invoices suspected of duplication, mismatch, or fraud.
Overview
Invoice Risk Detection is an application built on the Tire Extraction platform. AI reads invoices from photos and PDFs, parses them into structured data (supplier, tax ID, date, invoice number, line items, tax rate, and total), then cross-checks them internally and against historical data to score risk for each invoice. The system flags suspicious signals such as duplicate invoices, mismatched tax IDs, incorrect total calculations, or anomalous suppliers, with concrete reasons so accountants can review quickly.
- Tech stack
- PythonFastAPIPyTorchOCRlitellm
- Focus
- OCRInvoicesRisk scoringFraud detection
Key features
Parse invoices into structured data
From a photo or PDF, AI extracts supplier, tax ID, date, invoice number, line items, tax rate, and total as clean fields, with a confidence score per field.
Risk scoring with explanations
Each invoice gets a risk score from low to high with concrete reasons, so accountants focus on reviewing the suspicious ones first.
Duplicate and anomaly detection
AI cross-checks against historical data to detect duplicate invoices, mismatched tax IDs, incorrect total calculations, and anomalous suppliers.
One platform, applied across many use cases
For use cases that involve understanding and extracting the information inside documents.
Incoming invoices
- Supplier
- Tax ID
- Invoice date
- Total amount
- VAT
Credit dossiers
- Customer details
- ID number
- Address
- Income
- Employment details
Financial statements
- Revenue
- Total assets
- Profit after tax
- Financial ratios per period
Vehicle documents
- License plate
- VIN
- Engine number
- Make
- Year
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