Fraud Detection

On products where fraud detection is enabled, each eligible 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, when it runs, 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.

Uploaded

The claimant attaches the document to the claim.

Classified

AI identifies the document type: payslip, IBAN, ID card, and so on.

Data extracted

Key fields are read from the document: amounts, dates, names, identifiers.

Fraud analysis

The checks below run on the file and its extracted fields, producing a score and a verdict.

Result forwarded

Once the verdict is final, it is sent to the insurer alongside the document.

The signals it looks for

The analysis is Korint’s own — there is no third-party fraud vendor. Findings combine Korint’s AI checks with in-house rule-based checks across several layers: the document’s content and identity fields, its digital history, the pixels of the image itself, and an AI reading of the document as a whole. The checks below always run on every eligible document.

Check	Layer	What it looks for
AI document examiner	AI vision	Reads the whole document like a trained examiner: typography consistency, completeness of the expected content, and signs that the document was generated or altered by AI. This is a primary tamper signal.
Partner checklist (vision)	AI vision	Applies the documentary-fraud rules specific to the partner, including provider and geographic-distance plausibility (for example, a repair invoice from a garage implausibly far from the incident).
Pixel forensics	Pixel tampering	Runs on every image: Error Level Analysis highlights regions re-saved or edited after the original, and copy-move detection finds areas duplicated within the same image to cover or fabricate content.
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.

Experimental checks

The following pixel-level checks are experimental and off by default. They are enabled selectively and are not currently active for the live partner.

Check	Layer	What it looks for
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.

Clean

No meaningful signal. The document looks consistent and untouched.

Review needed

One or more signals worth a human look before deciding. Not a conclusion.

Fraud suspected

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.

When the analysis runs

Fraud detection is enabled per product and per partner, so it does not run on every claim across the platform — today it runs for a single partner. On products where it is enabled, a document is still skipped from analysis when:

The file is infected — a document flagged by the antivirus scan is never analysed.
The claim is below the minimum amount — claims under a configured threshold (for example €500) skip analysis, so low-value claims are not held up.

On the roadmap

Per-partner geographic and amount plausibility already ship today through the partner checklist check. 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
Geographic plausibility	Shipping (per partner)	Are the distances between the declared locations realistic for the incident described?
Amount / provider-distance coherence	Shipping (per partner)	Do the amounts reconcile, and is the provider plausibly located for the declared incident?
Cross-document date coherence	Next	Do the dates across every document in the claim tell one consistent story?
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.

​What happens to each document

​The signals it looks for

​Experimental checks

​The score and the verdict

Clean

Review needed

Fraud suspected

​Your verdict comes first

​The AI summary

​When the analysis runs

​On the roadmap

What happens to each document

The signals it looks for

Experimental checks

The score and the verdict

Your verdict comes first

The AI summary

When the analysis runs

On the roadmap