Expense Fraud, Caught at Submit Time
Each expense is scored 0–100 across duplicate, amount-mismatch, altered-receipt, out-of-policy, and pattern signals, then routed clean, review, or blocked. A flagged expense can never auto-approve - it goes to a human.
Illustrative product view
Deterministic first, AI only to raise risk
The integrity engine is deterministic and golden-vector tested, so the same expense always scores the same way and every decision is explainable. An optional AI seam can add signal - but by design it can only raise risk, never lower it, so the model can never talk a fraudulent expense into approval.
What it scores
- Duplicate - the same receipt or expense submitted more than once
- Amount mismatch - the claimed amount doesn’t match the receipt
- Altered - signs the receipt image was edited
- Out-of-policy - over a limit or against policy
- Pattern - behavior that looks like structured abuse over time
Frequently asked questions
What is receipt integrity?
It is an expense-fraud shield that scores every expense 0–100 across duplicate, amount-mismatch, altered, out-of-policy, and pattern signals, then routes it clean, review, or blocked at submit time.
Can a flagged expense still be auto-approved?
No. A flagged expense can never auto-approve - it is always routed to a human. Only clean, in-policy expenses are eligible for auto-approval.
Is the scoring explainable?
Yes. The engine is deterministic and golden-vector tested, so scores are reproducible and each carries a reason. Any AI assistance can only raise risk, never lower it.
Where does it run?
At the point of submission, before approval, so fraudulent or duplicate expenses are caught before they enter the approval flow rather than after reimbursement.
Stay in the loop
One practical finance briefing a week - new guides, checklists, and benchmarks.
Put a decision - with proof - in front of every action
See how Fintra decides allow / step-up / hold / block per action and writes each verdict to a tamper-evident ledger.
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