Invoices, expense reports, CSE receipts: why human control still decides
What machines can read, and what they cannot judge
OCR and vision models read an invoice, an expense report or a works council (CSE) receipt in seconds: amount, date, issuer, VAT rate. This reading is fast and reliable: that is the role of EVA at TEVASOFT.
But reading a document is not the same as judging its validity. A consistent amount, a clean layout, a SIRET number that exists in official registries: none of this proves that an expense actually happened. This is exactly where control shifts from a reading task to a judgment task, and where humans remain decisive.
Supplier invoices: audit authenticates, humans arbitrate
On an invoice, automated audit checks the issuer against official registries (SIRET, intra-community VAT), cross-references amounts with orders or contracts, and flags format anomalies typical of falsified documents. But deciding whether an atypical invoice (a round amount, an unusual service, a new vendor with no history) should be paid remains a business judgment call only a human can make.
Expense reports: audit 100%, decide on flagged cases
On expense reports, volume makes exhaustive manual review impossible: a controller cannot re-read every restaurant or taxi receipt. EVA automatically audits 100% of lines (amount consistency, duplicates across employees, AI-generated fake receipts) and only surfaces genuinely doubtful cases. The controller doesn't become a receipt reader: they become the arbiter of flagged files.
| Control step | What the machine does | What humans decide |
|---|---|---|
| Reading the receipt | Reliable, instant OCR extraction | N/A |
| Consistency & duplicates | Systematic detection across 100% of documents | N/A |
| Edge or unusual case | Anomaly flagged | Decision to pay, reject, or request more evidence |
| Responsibility for the decision | None | Full |
Works council (CSE) receipts: same dynamic, larger scale
For a French works council (CSE), every holiday voucher, stay or sports membership must be checked: named beneficiary, cap, eligibility, proof of payment. An elected member, often a volunteer, cannot manually check hundreds of documents at the September peak without either cutting corners or falling weeks behind.
Automated audit pre-checks 100% of receipts and applies ASC rules and URSSAF thresholds without fatigue or oversight gaps. But the final decision (granting or denying a subsidy to a specific employee, on a borderline case) remains the elected member's responsibility, backed by the audit trail as evidence.
An audit engine can flag an inconsistency, never take on the responsibility for a payment, rejection or subsidy decision. That responsibility stays human: it is what exposes the company or the committee in a URSSAF or tax audit.
Control isn't a choice between human and machine
Invoices, expense reports, CSE receipts: in all three cases, the question isn't replacing human control with AI, but concentrating it where it adds value. Automated audit absorbs volume and repetition; humans decide the cases that genuinely require it.
Read more: receipt fraud, the CSE's blind spot and duplicate expense reports, a silent fraud.
Want to see how EVA splits the work between automated audit and human decision on your own documents? Contact us.