DOCUMENT FRAUD DETECTION

Evidence AI Detects

Analyse documents, metadata and evidence to uncover tampering, inconsistencies and suspicious patterns — helping your teams make faster, evidence-led decisions.

THE PROBLEM

A document can look genuine and still tell
the wrong story.

Document-based processes often rely on information that may be altered, inconsistent or difficult to verify.

Hidden Manipulation

Changes to document content or visual elements may not be obvious during manual review.

Inconsistent Evidence

Information across documents may contradict one another. ‎
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Duplicate or Reused Documents

The same or similar documents may appear across different
submissions.

Too Much to Review

High volumes make detailed manual examination difficult and inconsistent.
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Fraud rarely reveals itself through one signal.
The clues are often hidden across the document, its metadata and the surrounding evidence.

HOW EVIDENCE AI WORKS

One evidence pipeline. Multiple fraud signals.

Analyse

Correlate

Assess

Review

Visual & Pixel Analysis

  • Detect suspicious visual alterations and anomalies.

Metadata Analysis

  • Examine document properties and metadata for unusual patterns.

OCR & Document Understanding

  • Read and interpret document content.

Format & Structural Analysis

  • Identify unusual document structures or formatting.

Cross-Document Consistency

  • Compare information across related documents.

Duplicate Detection

  • Identify duplicate or reused content.

Data & Transaction Patterns

  • Identify suspicious relationships or patterns where relevant.

Evidence + Risk Indicators

  • Bring multiple signals together into a reviewable case.

Human-in-the-Loop

  • Give the reviewer clear evidence and control over the final decision.

EVIDENCE, NOT JUST A SCORE

Give reviewers evidence they can act on.

Instead of presenting only a risk score, Evidence AI can surface the signals and evidence behind the finding.

AI highlights what deserves attention.

Investigate

Request clarification

Approve

Reject

Potential Issue

Information in Document A differs
from that in Document B.

Use Cases

Where document fraud detection matters.

Apply evidence-led review wherever critical decisions depend on submitted documents.

 

BUSINESS VALUE

Reduce risk without
slowing the business.

  • Earlier Risk Detection Identify suspicious documents before they move deeper into the process.
  • Faster Review Focus investigators on documents and cases that warrant attention.
  • More Consistent Assessment Apply multiple detection methods consistently across submissions.
  • Lower Manual Effort Reduce the need for exhaustive manual document inspection.
  • Stronger Evidence Provide traceable indicators to support human investigation and decisions.
  • Scale Fraud Screening Analyse higher volumes without relying entirely on manual review.

HUMAN + AI

AI-assisted detection. Human-controlled decisions.

The organisations moving fastest with AI are not those with the most advanced models —they are those who built a governed information backbone first. Five properties define that foundation.

Evidence AI supports the reviewer — it does not replace the reviewer. This is particularly important for banking, insurance and other regulated environments.

Could your documents be

Discover how Evidence AI can strengthen document verification, surface suspicious patterns and give your teams stronger evidence for investigation and decision-making.