Fraudulent Invoice & Receipt Detection

The Cost of Fraudulent Claims

10-20%

Of all P&C claims are fraudulent.

$308 Billion

The amount paid out on fraudulent claims in the US each year.

Stop Paying out on Fraudulent Invoices

Invoice fraud doesn’t wait for audits. Every week, carriers lose thousands to doctored receipts, regenerated PDFs, or clever AI edits that slip through manual checks.

Inaza Central’s Fraudulent Invoice Detection module spots the red flags before a payout is made, so your claims team can focus on genuine cases, not digital counterfeits.

Prevent payouts on manipulated or AI-generated invoices

Catch metadata inconsistencies instantly

Explainable indicators your compliance team can defend

Deploy via drag-and-drop UI or API.
No IT overhaul

How to Use Inaza Central

From Problem to Solution in Minutes - not months.

Signup to Inaza Central

Get Started Instantly

Sign up and get started in minutes with our pre-built solutions, solving core tasks out of the box

Find Your Solution

Select the Fraudulent Invoice Detection Solution

Browse and test solutions based on your needs, whether it’s claims fraud, underwriting cleanup, or fleet submission automation. All tools are transparent, tunable, and instantly testable.

Win in Minutes

Deploy Into your Workflows

When you're ready to go live, Inaza Central provides fully documented, production-grade APIs for every solution. Whether you're automating invoice validation, underwriting data ingestion, or claims triage - everything is available in a format your developers can implement in hours, not weeks.

Underwriting Automation

FNOL & Fraud Automation

Key Capabilites

Smarter Detection, Built for Real-World Fraud

Claims teams shouldn’t have to play forensic accountant. This Inaza Central solution automatically identifies document edits, calculation errors, and metadata inconsistencies that indicate manipulation, without slowing down approvals or overloading adjusters.

Physical tamper detection

Catch the human touch in digital form. Detect whiteout marks, handwriting additions, and altered line items - across scans, photos, or PDFs.

Metadata anomaly analysis

Inspect file properties and hidden data for red flags - timestamp mismatches, device ID swaps, inconsistent file structures, and re-exported PDFs. It surfaces subtle discrepancies invisible to human review.

Mathematical consistency checks

Automatically verify invoice totals and line-item sums. The engine spots rounding discrepancies, mismatched tax calculations, and arithmetic errors that often signal manual manipulation or duplicate entries.

Gen AI content detection

Distinguish genuine documents from AI-generated fabrications. The model identifies synthetic textures, font irregularities, and metadata artifacts unique to genAI-created or regenerated invoices.

Threshold tuning & explainable scoring

Adjust detection sensitivity to align with your fraud tolerance. Every score includes clear indicators, confidence levels, and visual evidence so compliance and SIU teams can defend each decision.