การตรวจสอบรายงานค่าใช้จ่าย AI

An AI expense audit starts by reading each receipt. Models extract the merchant, date, amount, tax, currency and, for documents like hotel folios, individual line items. The system then corroborates the claim: does a matching corporate card transaction exist, does the date fit the employee's travel itinerary, and does the amount fit per-diem limits for that city? Policy rules check items such as alcohol, class of travel, weekend spending and missing attendee names. Machine learning adds risk scoring, comparing spending with employees in the same role and location to find unusual patterns. Products in this space include AppZen, SAP Concur's audit services, Expensify, Ramp, Brex, Navan and Emburse. The big change from traditional practice is coverage. Manual audit teams typically sampled a small share of reports; software can review every report and send only the flagged ones to people. Fake receipts are an old problem, since receipt templates have long been available online, but image generators that render realistic text make fabrication easier. Visual inspection and AI-image detectors are unreliable against well-made fakes. Some generators attach provenance metadata such as C2PA content credentials, but that metadata is easily removed by taking a screenshot or re-saving the file. The strongest defense is corroboration with independent data, above all the corporate card feed, plus consistency checks: tax math, whether the merchant exists, whether the layout matches that merchant's real receipts, and whether the timing fits the trip. A common misconception is that a detector can reliably tell whether a receipt image is AI-generated. Another is that a flag proves wrongdoing. Flags are prompts for review, and many have innocent explanations such as a lost receipt or a merchant with an unusual billing name.