AI-Generated Fake Receipts Account for 14% of Expense Fraud Attempts

TL;DR: Businesses face increasing expense fraud using AI-generated receipts, with AppZen reporting 14% of September fraudulent submissions used AI-created documents compared to zero last year. Expense platforms attribute the surge to accessible image-generation models like OpenAI’s GPT-4o, which enable employees to create convincing fake receipts in seconds.

The democratisation of AI image-generation technology has created a significant new vector for corporate expense fraud, with multiple expense management platforms reporting substantial increases in AI-generated fake receipts since early 2025.

Scale and Timing of the Problem

Fraud Statistics:

  • AppZen: 14% of fraudulent documents in September 2025 were AI-generated (0% previous year)
  • Ramp: Over $1 million in fraudulent invoices flagged within 90 days of deploying new detection software
  • Medius survey: 30% of US and UK financial professionals report increases in falsified receipts since GPT-4o launch

Multiple platforms identified March 2025—when OpenAI launched GPT-4o’s improved image generation model—as marking a significant jump in AI-generated receipt submissions.

Technical Capabilities and Accessibility

The emergence of free, accessible image-generation software has fundamentally changed expense fraud dynamics. Previously, creating fraudulent documents required photo editing skills or purchasing services from online vendors. Current AI tools enable employees to generate convincing fake receipts in seconds using simple text instructions to chatbots.

Quality of Generated Receipts:

  • Realistic paper textures including wrinkles and creases
  • Detailed itemisation matching real restaurant menus
  • Convincing signatures and handwriting
  • Appropriate formatting and branding elements

Chris Juneau, senior vice-president and head of product marketing for SAP Concur, emphasised the sophistication: “These receipts have become so good, we tell our customers, ‘do not trust your eyes.’” SAP Concur processes over 80 million compliance checks monthly using AI.

Detection Challenges and Countermeasures

The realistic quality of AI-generated receipts has rendered human review insufficient, forcing organisations to deploy AI-based detection systems.

Detection Methods:

  • Metadata analysis: Scanning images to identify AI platform signatures
  • Contextual verification: Cross-referencing server names, timestamps, and employee travel patterns
  • Consistency checks: Identifying repetition patterns across multiple submissions

Detection Limitations: Metadata-based detection can be easily circumvented by users taking photographs or screenshots of AI-generated images, which removes the identifying metadata. This vulnerability necessitates multi-layered detection approaches combining technical and contextual analysis.

Calvin Lee, senior director of product management at Ramp, noted the advantage of automated systems: “The tech can look at everything with high details of focus and attention that humans, after a period of time, things fall through the cracks, they are human.”

Executive and Industry Perspectives

SAP research from July 2025 found nearly 70% of chief financial officers believed employees were using AI to falsify travel expenses or receipts, with approximately 10% certain such fraud had occurred in their organisations.

Mason Wilder, research director at the Association of Certified Fraud Examiners, characterised AI-generated fraudulent receipts as “a significant issue for organisations,” highlighting the elimination of technical barriers: “There is zero barrier for entry for people to do this. You don’t need any kind of technological skills or aptitude like you maybe would have needed five years ago using Photoshop.”

Platform Provider Response

OpenAI told the Financial Times that it takes action when policies are violated and that its images contain metadata signalling ChatGPT creation. However, the ease with which this metadata can be removed limits its effectiveness as a sole deterrent.

Sebastien Marchon, chief executive of expense management platform Rydoo, emphasised the current nature of the threat: “This isn’t a future threat; it’s already happening. While currently only a small percentage of non-compliant receipts are AI-generated, this is only going to grow.”

Looking Forward

The trajectory suggests expense fraud detection will increasingly become an arms race between generation and detection AI systems. Organisations must implement multi-layered verification approaches combining:

  • Technical metadata analysis
  • Contextual trip and expense pattern verification
  • Anomaly detection across historical submission patterns
  • Employee behaviour analytics

The accessible nature of AI image generation means this issue extends beyond large corporations to organisations of all sizes. The “zero barrier for entry” means any employee with internet access can now create convincing fraudulent documentation, fundamentally changing the risk profile for corporate expense management.

This development highlights broader questions about AI democratisation, responsible deployment, and the challenges of governing dual-use technologies that offer legitimate benefits whilst enabling new forms of misconduct.

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