AI FRAUD DETECTION
Six AI agents. Two configurable controls.
Zero fraud through the door.
REME's fraud detection engine runs at submission, not at audit. Six AI agents check every claim in parallel. Two configurable controls let your finance team enforce policy their way. Nothing gets to approval without passing both. Your fraud losses drop systematically over your first twelve months.
Under 200ms per claim · ISO 27001:2022 · Six fraud categories · Extensible controls layer
Claim #4729 · $342.00 · Sales
Client dinner · 18 Jul 2026
Duplicate Detection
No prior submissions found
Handwritten Validation
Printed receipt, no handwriting
Currency Mismatch
SGD on receipt, SGD claimed
Out-of-Country Check
Singapore — authorized location
Disallowed Multi-Currency
Single-currency claim, allowed
Data Mismatch
Amount differs from receipt by 7%
⚠ ROUTED FOR REVIEW — 1 of 6 agents flagged
Total processing time: 145ms
HOW IT WORKS
Two layers, one gate
REME runs two parallel checks on every expense claim, before approval. Layer one is AI: six fraud detection agents look for patterns your team would miss even with unlimited review time. Layer two is rules: your finance team configures the specific policy checks you already enforce manually today. A claim only gets through if it passes both. Nothing gets approved that shouldn't have been.
Layer 1: Six AI fraud agents
Pattern-based detection your team can't scale manually. Trained on cross-industry expense data. Runs in under 200 milliseconds per claim.
Layer 2: Configurable finance controls
Your policy, your rules. High-risk vendor blocklists, pre-approved auto-approval thresholds. New controls added quarterly based on customer requests.
Full details on the controls layerThe six fraud categories caught at submission
Every claim runs through all six checks in parallel. Any failure flags the claim for finance review. Every check is explained on its own detail page (linked from each card).
Duplicate detection
Same receipt submitted twice, sometimes photographed from different angles or with slight modifications. Our engine matches across image forensics, amount patterns, date proximity, and vendor identity. Catches near-duplicates human review misses.
Handwritten claim validation
Handwritten receipts are high-risk because amounts can be inflated after the fact. Our engine reads handwritten text with confidence scoring, cross-checks against employee history and vendor patterns, and flags anomalies for finance review.
Currency mismatch detection
Receipt shows one currency, claim is submitted in another. Common in cross-border teams. Our engine detects the mismatch and validates against the correct exchange rate at the transaction date. Catches deliberate manipulation and honest mistakes both.
Out-of-country claim flagging
Claim originates from a country where the employee is not authorized to spend, or from a location that does not match a business trip in the calendar. Our engine flags these claims for finance review before approval.
Disallowed multi-currency validation
Some companies allow multi-currency expenses only for authorized travelers or specific policies. Our engine validates each multi-currency claim against your company’s allowlist. Unauthorized multi-currency claims are flagged automatically.
Data mismatch detection
The claim amount does not match the receipt total. The vendor on the receipt does not match the vendor on the claim. The date does not match the trip. Any mismatch between what our OCR extracts and what the employee submits gets flagged.
SEE IT IN ACTION
See it in action
Three of the six fraud categories are shown below. Watch how the detection actually happens.
Duplicate detection in action. How the same receipt gets caught even when submitted from different angles or with modifications.
How currency mismatches slip past manual review, and how REME's engine catches them at submission.
Why handwritten receipts are the highest-risk category for expense fraud, and how our engine validates them.
CONFIGURABLE CONTROLS
Your policy, your controls, extensible over time
Beyond AI pattern detection, REME gives your finance team configurable controls to enforce policy your way. Two controls are live in production today. New controls are added quarterly based on customer requests.
Live today
High-risk vendor and threshold rules
Maintain a list of high-risk vendors, categories, or amount thresholds. Any claim matching an entry gets flagged for manual review before approval. Common uses: block claims from unapproved vendors, flag any claim above a specific amount, enforce mandatory review on flagged categories.
Auto-approval for pre-approved routine claims
Set your pre-approved vendors and pre-approved amount thresholds. Small routine claims (client coffee under 15, taxi under 30) flow through automatically. Your finance team's time is saved for the claims that actually matter. Employees get reimbursed faster on routine expenses.
Added quarterly based on customer requests
Category ratio rules
Enforce percentage limits across expense categories. Example: alcohol spend must not exceed 20 percent of food spend on the same claim.
Location-aware rules
GPS-based restrictions on specific expense types. Example: no taxi claims where the origin or destination is the employee's office.
Time-of-day rules
Weekend and after-hours claim restrictions. Example: no meal claims outside standard business hours without pre-approval.
If your finance team runs a specific policy check manually today that you wish were automatic, mention it during your demo. Recent customer requests get evaluated for the next quarterly release.
For full controls detail including recent releases and roadmap, see our dedicated configurable controls page.
THE ROI STORY
The measurable ROI: fraud losses drop, quarter by quarter
The ROI story is not 'we caught X in fraud last month.' It is 'we had X fraud losses in Q1 and Y in Q4, and Y is much smaller than X.' The curve is predictable, measurable, and repeatable across our customer base.
Baseline: see your real exposure
Our engine catches fraud and duplication your team was missing. Most finance leaders discover their actual fraud exposure is higher than they estimated. This becomes your starting point, measured in your own data, in your own currency, on your own claim volume.
Systematic reduction
Three things happen simultaneously. Employees learn what gets caught and fraud attempts drop. Finance tightens custom controls based on what our engine surfaces. Our AI learns your specific vendor patterns and gets more precise. Fraud losses drop quarter by quarter.
Sustained low fraud
Behavioral change (employees stop trying) and detection capability (our engine catches remaining attempts) work together. Fraud losses stay at a stable low level. New employees inherit the environment and adjust from day one.
FAQ
Questions we get asked most often
Most expense platforms catch fraud during audit, weeks or months after the money is out the door. REME catches fraud at submission, before approval. Six AI fraud detection agents plus configurable finance controls run on every claim in under 200 milliseconds. Nothing gets to approval that shouldn’t have.
Duplicate detection (same receipt submitted twice), handwritten claim validation (amounts inflated on handwritten receipts), currency mismatch detection (receipt in one currency, claim in another), out-of-country claim flagging (unauthorized locations), disallowed multi-currency validation (against your allowlist policy), and data mismatch detection (receipt amount does not match claim amount).
Your finance team sets policy the way you already enforce it manually today. High-risk rules let you maintain vendor blocklists, category restrictions, or amount thresholds that force manual review. Auto-approval lets you pre-approve vendors and amounts for routine claims that flow through automatically. The controls layer is extensible: new controls are added quarterly based on customer requests.
Bring it to your demo. The controls layer is architected for extensibility. Recent customer requests we have added include category ratio rules and location-aware rules. If your specific policy check is not covered today, we evaluate it for the next quarterly release. Some are added in weeks, some in months, depending on complexity.
Measurable outcomes, not vague promises. Baseline your fraud exposure over months one through three. Watch losses drop quarter by quarter as employees learn what gets caught and our AI learns your patterns. By month twelve, fraud losses are systematically lower and stay there. All measured on your own claim data, in your own currency.
All six AI agents plus both configurable controls run in parallel, in under 200 milliseconds per claim. This is fast enough that employees do not experience delay when submitting. Even at claim volume spikes (month-end close, quarterly reviews), throughput does not degrade.
Finance sees the flag with the specific reason. Examples: "possible duplicate of receipt submitted three days ago," or "receipt amount does not match claim amount by more than 5 percent," or "vendor is on high-risk list." Finance can resolve directly with the employee in-platform, without leaving the system. Most flags clear in minutes rather than days.
Yes, and this is intentional. Transparency is part of what drives the fraud reduction curve. Employees see when a claim is flagged, why, and what needs to change for it to be approved. Legitimate claims get corrected and resubmitted quickly. Fraudulent attempts stop being attempted.
Three channels equal-status: WhatsApp (photo of receipt to your company number), email (forward to your company address), web upload (drag-and-drop or click). All three feed the same fraud detection engine. Custom channels like Slack, Teams, or SMS can be added on request, typically in two to four weeks.
Start a free one-month trial at app.reimburseme.ai to explore the platform yourself. Or book a twenty-minute demo where our team walks through the six fraud detection categories and configurable controls tailored to your workflow. Bring examples of the fraud patterns you have seen historically, and we can walk through how each would be caught.
See it work on your own expense data
A twenty-minute demo walks through the six fraud categories, the configurable controls layer, and how the twelve-month reduction curve plays out on your specific claim volume and industry.
Researching the broader category? Read our complete guide to expense management software.