Fake Receipts 2.0: Why Human Audits Fail Against AI and How Tech Is Fighting Back

The travel and expense (T&E) world is facing a new class of fraud: AI-generated receipts so realistic they are evading traditional human audits. Verify -- SAP Concur’s AI-powered auditing solution -- has helped customers identify thousands of AI-generated receipts, and the detection rate is roughly 18 times higher than earlier checks that focused only on known online receipt generators.

While fake receipts have long been a problem, and only a seemingly small percentage of employees are submitting fake receipts, this can still translate into meaningful losses when scaled across large organisations. The operational fallout is broad: audit teams are overwhelmed, accounts-payable staff face heavier workloads, the integrity of corporate travel programs is undermined, and mishandled investigations can erode employee trust or create legal exposure.

“With Verify’s AI receipt detection, about 1% of reviewed receipts have been flagged as potentially AI-generated, created by different image generators, including ChatGPT, Gemini and Stable Diffusion,’ said Chris Juneau, senior vice president, head of Product Marketing at SAP Concur. “AI isn’t increasing the frequency of expense fraud, but it is changing how it occurs.”

The economics and risks of expense fraud have changed, and the difficulty in identifying AI fakes makes manual review insufficient.

How AI-generated receipts bypass human review

Generative AI models increasingly excel at visual realism: they produce plausible logos, fonts, itemised entries, GST or VAT lines, and totals that look “right” at a glance. AI models can be asked to strip or fake metadata (EXIF fields, timestamps, geolocation) making conventional red flags disappear. Add scale and variety, multiple AI models producing different styles of receipts, and pattern-spotting by human auditors becomes impractical. Finally, the psychological realism of these receipts reduces natural suspicion and allows fraudulent claims to slip past reviewers.

Metadata Forensics and AI Detection to Reduce Fake Receipt Generation

With nearly 70% of expense transactions including an attached receipt, human audits alone can’t win this contest. Leading solutions combine two complementary approaches:

1. Metadata forensics: By partnering with image-forensics specialists, audit vendors can detect signatures and abnormal patterns in image metadata that are difficult for attackers to mimic consistently. These signals include subtle artifacts in EXIF data and generation footprints that betray AI origin.
2. AI detection: Models trained on a large body of historical receipts, including confirmed AI-generated examples, can score and flag suspicious images. Continuous retraining and partner-sourced intelligence keep detection current as fake receipt generation continues to change.

Together, these approaches scale far beyond human review, reduce false negatives, and allow auditors to focus on higher-risk cases. Most importantly, as AI receipt creation continues to evolve, AI detection solutions can also evolve to keep pace. Verify is one example of an AI-driven solution that combines metadata forensics, AI scoring, and continuous updates from ecosystem partners to detect and prioritise suspicious receipts without relying on brittle manual checks.

Finance, travel, and compliance teams are already adapting by tightening submission policies, making e-receipts mandatory, and integrating automated tools into policy enforcement.

But there is no doubt AI-generated receipts are a growing, hard-to-detect threat that changes the calculus of expense fraud. The answer is not more manual review but smarter, data-driven defenses: metadata forensics, machine-learning detection, ethical validation workflows, and cross-industry collaboration. If you manage T&E risk, now is the time to audit your policies, engage your T&E vendor, and build detection into your expense process before fraud scales further.

Frequently Asked Questions:

What are AI-generated receipts?
AI-generated receipts are images or PDFs created by generative AI models (e.g., ChatGPT, Gemini, Stable Diffusion) that mimic real receipts including logos, item lines, GST/VAT/totals and other visual cues.

How do AI-generated receipts evade human audits?
Modern generative AI models produce highly convincing visual details and can alter or remove metadata eliminating common red flags. The ability to mimic a wide variety of receipts also defeats human pattern-spotting, and the realism reduces reviewer suspicion, making human inspection inadequate at scale.

How are these fakes detected effectively?
Effective defenses pair metadata forensics (looking for subtle EXIF anomalies and generation footprints) with machine-learning detectors trained on large body of confirmed real and AI-generated receipts. Continuous retraining and partner-sourced threat intelligence keep models current, and combined scoring prioritises high-risk cases for human follow-up.

What practical steps should organisations take now?
Tighten submission rules (for example, require e-receipts where possible), integrate automated detection and metadata-forensics into expense workflows, prioritise suspicious claims for focused investigations, and work with vendors who continually update models and partner intelligence. Also audit T&E policies, train staff on new fraud patterns, and ensure investigations preserve employee trust and legal compliance.

How do you balance stronger detection with employee trust and operational burden?
Use a risk-based approach: automated scoring should reduce manual work by flagging only higher-risk submissions for review, while clear policies and transparent communication about why certain receipts are questioned help maintain trust. Follow ethical validation workflows, document decisions, and ensure appeals or clarifications are easy for employees to provide.