Fraud Investigation & Document Fraud Detection Software

Industries

Pick your industry, see where the fraud actually hides

The fraud is in the file, not the transaction. Loan applications, mortgages, leases, letters of credit and shipping papers verified before funding, which is where altered statements and reused paperwork actually enter.

Altered files flagged. Integrity assessment catches documents that were edited after issue.
Reused paperwork surfaces. Near-duplicate detection compares against prior applications, not just within one.
Clear files keep moving. Only what fails routes to a reviewer; the rest flows on to origination.

SIU stops re-watching the whole file. Photographs, dashcam and surveillance footage, recorded statements and estimates in one evidence record per claim, questioned rather than reviewed end to end.

Ask across the claim. Natural-language questions over the file, with the source shown.
Statements against the evidence. Recorded statements transcribed and checked against the documents and footage.
Referral-ready. De-identified for release, with custody logged, when it goes outside.

Eligibility fraud arrives as paperwork. SNAP, Medicaid, TANF and WIC files checked for altered pay stubs, duplicated household documents and inconsistencies across applications.

Duplicates across households. The same document appearing on unrelated applications surfaces.
Reads what claimants send. Photographs of documents and poor scans, not clean PDFs.
Inside the agency boundary. On-premises or private cloud where policy will not allow otherwise.

The defence side of a liability claim. Dashcam and incident footage held with integrity intact, so a staged or exaggerated claim can be answered rather than quietly settled.

Custody that survives. Chain of custody from ingest through to disclosure.
Legal hold. Evidence preserved the moment a claim looks like litigation.
Shared without exposure. Portals with de-identification for opposing counsel.

What You Get

What examining the file actually takes

Verification, not transaction scoring

This complements the detection platform rather than replacing it. It examines the file behind the alert, which is the part those systems do not do.

Complements detectionDocument-level

Reads what people actually submit

OCR with handwriting recognition handles poor-quality scans, photographs of documents and mixed paperwork, not the clean sample in a demo.

OCR + ICRPoor scans

The resubmitted file is caught

Exact and near-duplicate detection compares against prior applications, which is where reused paperwork is normally missed.

DeduplicationCross-application

Cross-document consistency

Agentic workflows compare names, amounts, dates and terms across the whole set and score the file, routing only what looks wrong to a person.

Agentic workflowsConfidence scoring

Every format in one record

Documents, photographs, dashcam and recorded statements held together per case with integrity verification and chain of custody.

Chain of custodyFile assessment

Runs in your environment

Deployable in your own cloud, on-premises or air-gapped, which is usually a condition of handling case material at all.

On-premisesAir-gapped

How It Works

From submitted file to defensible package

Take in the file

Documents, images, footage and recorded statements ingested per case, with integrity verified and duplicates flagged at the gate.

Examine and cross-check

OCR and transcription make everything readable; agentic workflows compare fields across the set, score the file and surface what does not reconcile.

Route, package, evidence

Clear files flow on to the originating system; suspicious ones reach a reviewer, and a referral leaves de-identified with custody logged.

FAQ

Fraud investigation, asked and answered

What is fraud investigation software?

It is the tooling used after a fraud is suspected: examining the documents that were submitted, the footage and photographs on the file, and the statements that were recorded, then building a package that survives referral. It is distinct from fraud detection, which scores transactions or identities in real time to raise the alert in the first place.

How is this different from a fraud detection platform?

Detection platforms score transactions, identities and onboarding attempts and raise alerts. They do not read the loan file, watch the dashcam or listen to the recorded statement. This examines that material. It complements a detection platform rather than replacing it, and it is deliberately not transaction monitoring.

Does it do document fraud detection?

Yes, at the document level. Submitted files are read with OCR and handwriting recognition, signatures are detected and compared across the set, file integrity is checked for alteration, and exact and near-duplicate detection catches paperwork reused across applications. Agentic workflows then cross-check names, amounts, dates and terms for inconsistencies and score the file.

Can it catch a document that was reused on another application?

That is what near-duplicate detection is for. Submissions are compared against prior files rather than assessed in isolation, so the same bank statement or invoice appearing on a second application surfaces instead of passing because each file looks fine on its own.

Does it cover claims as well as lending?

Both. The same examination applies to a loan or trade-finance file and to a claim, and on the claims side it extends to photographs, dashcam and surveillance footage and recorded statements held in one evidence record per claim.

What happens when a case becomes a referral or litigation?

The package leaves with personal data removed on the shared copy while the original stays intact, and with an audit trail of who accessed what and when. That trail is built as the work happens rather than reconstructed afterwards.

Can it run inside our own environment?

Yes. Deployment covers your own cloud, on-premises and fully air-gapped, which is normally a condition of handling case material and personal data at this level.

Something Was Flagged. Now Someone Has to Prove It