A tampered lab report rarely announces itself. The hospital letterhead is real, the doctor's name checks out, and the formatting looks right — because most forgeries start from a genuine document. What gives them away is arithmetic: forged documents almost always contain numbers that cannot be true. This article walks through the layered checks modern claims systems use to catch them automatically.
Why lab reports are a favourite target
Health claims hinge on evidence, and lab reports are the evidence that is easiest to alter. Changing a single value — a haemoglobin count, a glucose level, a platelet count — can turn a routine check-up into grounds for a hospitalization claim. Unlike an identity document, a lab report has no hologram, no chip, and usually arrives as a photo or a PDF, several generations away from the original printout.
Manual review catches some of this, but reviewers are reading hundreds of documents a day. The checks below are deterministic — they run in milliseconds, they never get tired, and critically, they cannot hallucinate: each one is arithmetic or physics, not opinion.
Check 1: physiologically impossible values
Every analyte on a lab report has two ranges. The reference range printed on the report describes a healthy patient — haemoglobin of 13–17 g/dL, for example. But there is a second, much wider range: values that are possible in a living human at all. Severe anaemia can push haemoglobin down to 4 or 5 g/dL. Polycythemia can raise it past 20. A value of 45 g/dL cannot occur in a living person — it is not a sick patient, it is a typo or a forgery.
Automated plausibility checks encode these physiological bounds for common analytes — haemoglobin, WBC and platelet counts, glucose, creatinine, bilirubin, electrolytes, TSH — and flag any value outside them. The design principle is deliberate width: bounds are set at the extremes of survivable pathology, so a genuinely abnormal result never triggers a false alarm, while a fabricated number that a fraudster picked to look "very sick" frequently does. Unit handling matters here: 152 g/L and 15.2 g/dL are the same haemoglobin, so a robust checker converts units before judging, and skips values whose units it cannot identify rather than guessing.
Check 2: arithmetic that must reconcile
Hospital bills and itemized lab invoices carry their own proof of integrity: the math. Line items must sum to the subtotal. Subtotal plus GST minus discount must equal the grand total. Quantity times rate must equal each row's amount. When someone inflates one line item on a genuine bill, they almost never recompute every dependent figure — and a single mismatch is machine-detectable with certainty, not probability.
The same logic extends to dates: a discharge date before the admission date, a claim intimated before the date of death on the attached certificate, a report signed before the sample was collected. Fraud is often just bad arithmetic and impossible timelines.
Check 3: identifiers that self-validate
Many identifiers on Indian claim documents carry built-in check digits. GSTIN numbers on bills include a checksum character; Aadhaar numbers use the Verhoeff algorithm; passport MRZ lines carry their own check digits. An invented identifier fails these checks immediately — no registry lookup required. (We cover the Aadhaar case in detail in Aadhaar checksum validation, explained.)
Check 4: the file itself testifies
Beyond the content, the document file carries forensic signals. PDF metadata reveals editing software and modification timestamps inconsistent with the claimed issue date. Image forensics detect resampled regions where a value was pasted over. Perceptual hashing catches the same template being reused across multiple claims with only names and numbers changed. Reverse image search identifies letterheads lifted from stock templates. None of these alone is conclusive — together they form a weighted risk score.
Check 5: cross-document consistency
A claim file is a bundle: claim form, ID proof, lab reports, bills, discharge summary. The strongest fraud signals often live between documents — a patient name spelled differently on the bill and the report, a diagnosis on the discharge summary that does not match the tests billed, lab dates that fall outside the admission window. Checks that operate on the bundle catch what no single-document check can see.
Where AI fits — and where it should not
Large language models are excellent at reading messy scans, classifying document types, and extracting fields from unstructured layouts. They are the wrong tool for the final verdict: a model can misread, and an auditor cannot cross-examine a probability. The architecture that works keeps the roles separate — AI extracts, deterministic rules validate, and a human reviews anything the risk score escalates. Every flag carries its evidence: the exact value, the bound it violated, the sum that did not reconcile.
That separation is the difference between a system reviewers trust and one they learn to ignore. A flag that says "haemoglobin 45 g/dL exceeds the physiological maximum of 25" is defensible in an audit. A flag that says "the model thinks this looks suspicious" is not.
The takeaway
Tampered lab reports fail in predictable, testable ways. A claims pipeline that checks physiological plausibility, arithmetic reconciliation, identifier checksums, file forensics, and cross-document consistency catches the majority of crude forgeries automatically — and focuses human expertise on the sophisticated minority that remains.
Techies Universe's Document AI for insurance claims runs these checks as part of a governed pipeline with evidence-backed authenticity scores and human review routing. Talk to us if you would like to see it against your own claim documents.