> ## Documentation Index
> Fetch the complete documentation index at: https://docs.korint.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Fraud Detection
> How Korint analyses every document attached to a claim for tampering, scores it, and produces a short AI summary you can triage at a glance.
Every document attached to a claim is analysed automatically before it reaches a claims manager. This page explains what that analysis looks for, how to read the score and verdicts, and how the short AI summary at the top of each claim is produced.
## What happens to each document
Every supporting document runs through the same automated pipeline. Each document is analysed on its own, so a single altered payslip is never hidden by clean documents around it.
The claimant attaches the document to the claim.
AI identifies the document type: payslip, IBAN, ID card, and so on.
Key fields are read from the document: amounts, dates, names, identifiers.
The checks below run on the file and its extracted fields, producing a score and a verdict.
Once the verdict is final, it is sent to the insurer alongside the document.
## The signals it looks for
Findings combine the analysis provider's verdict with in-house checks across three layers: the document's content and identity fields, its digital history, and the pixels of the image itself.
| Check | Layer | What it looks for |
| ------------------------- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **MRZ reconciliation** | Identity documents | On an ID card or passport, decodes the machine-readable zone and compares it to the printed face: document number, expiry date, birth date. A mismatch is conclusive tampering. |
| **Field validation** | Content | Checks extracted values against their own rules, e.g. an IBAN that fails its mod-97 control key. |
| **Company registry** | Content | Reconciles a SIREN/SIRET against the official registry to confirm the named company exists and matches. |
| **Date consistency** | Content | Cross-checks dates within and across documents, e.g. a footer timestamp earlier than the transaction it describes. |
| **Digital history** | Metadata | File created, modified, or produced by editing software after the date it should have been issued. |
| **OCR glyph geometry** | Pixel tampering | Re-typed text: a word whose letter spacing, baseline, or character size no longer matches the rest of the line. |
| **Noise residual (PRNU)** | Pixel tampering | Inserted content: an area missing the faint, uniform sensor and paper grain present everywhere else on the page. |
| **Background continuity** | Pixel tampering | Erased or painted-over fields: a flat patch of uniform colour sitting inside a block of text. |
## The score and the verdict
Each document gets a score from **0 to 100**, where higher means more suspicious. The score maps to one of three verdicts you see on every document row.
No meaningful signal. The document looks consistent and untouched.
One or more signals worth a human look before deciding. Not a conclusion.
Strong evidence of tampering or a major inconsistency in the document.
## Your verdict comes first
The automated verdict is a starting point, never the final word. On any document you can set **Clean**, **Review needed**, or **Fraud** yourself and add a note. Your verdict overrides the automated one everywhere it is shown, and the claim summary updates to reflect it.
## The AI summary
At the top of each claim, a few short bullets restate the document findings so you can triage at a glance. Each bullet links to the document it refers to.
**It only restates, it never invents.** The summary is generated strictly from the findings already on the documents. It adds no conclusion, figure, or recommendation of its own. The per-document analysis remains the source of truth.
When you override an automated verdict, the summary notes that one or more verdicts were modified by a manager.
## On the roadmap
Today's checks score each document on its own. The next step is claim-wide reasoning that cross-references documents against each other and against the outside world. Directions we are building toward (subject to change):
| Direction | Status | Question it answers |
| --------------------------------- | --------- | ------------------------------------------------------------------------------------------------ |
| **Cross-document date coherence** | Next | Do the dates across every document in the claim tell one consistent story? |
| **Geographic plausibility** | Next | Are the distances between the declared locations realistic for the incident described? |
| **Amount coherence** | Next | Do the amounts reconcile across invoices, quotes, and the declared loss? |
| **Identity reconciliation** | Exploring | Name, date of birth, address, and IBAN holder agree across the ID, RIB, payslip, and claim form. |
| **Reused-document detection** | Exploring | The same receipt, photo, or PDF submitted across several claims, or a reused template. |
| **Incident corroboration** | Exploring | Cross-check the declared date and place against external data: weather, events, registries. |