AI Expense Fraud Detection
Catch It Before It's Paid, Not During the Audit Three Months Later.
REME runs seven specialised AI agents on every expense claim in under 200 milliseconds — before it reaches an approver. Duplicate receipts. Edited totals. Currency mismatches. Round-number gaming. All caught. All quarantined. Payment never happens.
How much is expense fraud costing your company right now?
The ACFE 2024 Report to the Nations: the median company loses 5% of annual revenue to occupational fraud. Expense reimbursement is one of the most common and hardest-to-detect vectors. A 200-person company loses approximately $47,000 per year before anyone notices a pattern.
Traditional expense tools — Concur, Expensify, Ramp — flag fraud during the audit, after payment has been made. By then, the money is gone. Recovery requires confronting the employee, escalating to HR, and often legal action. Most companies absorb the loss.
REME flips the order. Every claim is screened by seven specialised AI agents before it reaches an approver. Suspicious claims are quarantined for finance review. The fraudulent payment never happens.
Average annual fraud loss — 200-person company (ACFE 2024)
Can AI detect duplicate or fraudulent expense submissions?
Duplicate detection
REME uses perceptual-hash matching — the same technology used in reverse image search — to identify the same receipt submitted from different angles, different dates, or by different employees. Even if the receipt is cropped, re-photographed from a screen, or submitted weeks apart, the hash matches.
Image Forensics (Handwritten)
REME's Image Forensics agent checks for handwritten receipts and documents. It detects handwritten fields, manually filled amounts, and hand-drawn entries that may indicate fabricated or altered submissions — catching handwritten claims that would pass visual review.
Contextual fraud
The Contextual Grouper agent analyses patterns across all claims from an employee over time — identifying threshold-gaming, round-number patterns, and weekend "business" meals with no client named. Individual claims look clean. The pattern does not.
The seven AI agents: what each one catches
All seven run in parallel in under 200ms per claim. Before the approver sees it. Tap any card — agents with video demos show a live example.
REME's fraud engine self-learns from each company's historical data. The longer you use REME, the more accurately it identifies your specific team's patterns. A new company gets REME's baseline models. A company 12 months in gets a model trained on its own data.
How REME fraud detection compares to Expensify and Concur
Expensify / Concur
REME
Frequently asked questions
Catch expense fraud before it's paid, not after.
Seven AI agents. 200ms per claim. Self-learning. Beta access now open.
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