Payslips generated in seconds, bank statements recomposed from real data, tax notices retouched: producing a credible credit file no longer requires any particular skill, and the documents that come out pass human review without difficulty.
DeepFrauds Credit Conso examines every supporting document in the application before the credit decision, and returns an itemised verdict — not an opaque score.
Application fraud is caught at the moment the file is assessed. Once the money has gone out, retrospective review only documents a loss that has already happened.
The most frequently forged proof of income. Gross-to-net is recomputed in full, along with contributions, year-to-date accumulation and consistency across the months supplied.
Detects recomposed statements: balances that fail to carry forward, inserted transactions, broken layout, headers borrowed from a different document.
Structural checks on the document and reconciliation of declared income against the payslips supplied in the same application.
MRZ parsing to ICAO 9303, field coherence, retouching detection and identification of documents generated end to end by AI.
Utility bills, rent receipts, certificates: issuer verification, amounts and dates — frequently altered to attach an applicant to an address.
The final check: do income, employer, identity and address reconcile from one document to the next? This is where a fabricated file most often fails.
Document-reading systems extract fields from a payslip with remarkable reliability — including when the payslip is fake. They are built to understand content, not to interrogate whether the artefact is genuine.
An extraction engine returns "€2,480 net" with identical confidence on a genuine payslip and on a fabricated one. The forgery is invisible to it by construction, because a forgery is designed above all to be readable.
Identity checks confirm that the applicant exists and is not sanctioned. They say nothing about whether the documents that person submitted are real — and the dominant fraud pattern today pairs a genuine identity with fabricated supporting documents.
A rules engine recognises patterns it has seen before. Documents produced by generative models carry no prior signature — there is nothing to match against.
The document is treated as a physical artefact carrying a history: compression, fonts, metadata, arithmetic, generation signatures. The question stops being "what does this document say?" and becomes "is this document what it claims to be?"
Declining an application commits the institution. Every anomaly is named, located in the document it came from and tied to the test that produced it — which makes the decision documented and defensible.
Each signal appears with its severity and the document it came from — never as a number with no justification behind it.
Available by API, so the check sits inside the tools your underwriters already use, with no extra step for the applicant.
Every analysis is logged and retained, in an architecture designed to sit within GDPR, DORA and EU AI Act obligations.
Verify supporting documents across the entire inbound flow without adding time to the applicant's decision.
The segment most exposed to forged proof of income. Verification happens before approval, therefore before disbursement.
Secure the files you pass to lending partners and evidence the check you performed — which protects the relationship.
Embed verification by API inside the application journey, transparently for the applicant.
Through recomputation and through examination of the artefact. Gross-to-net, contributions and year-to-date accumulation are recalculated in full — a figure edited by hand almost always breaks an equality somewhere. In parallel, metadata, font coherence and pixel-level compression reveal retouching, while frequency analysis isolates documents generated entirely by AI.
An extraction solution pulls data out of a document; it does not rule on whether the document is authentic. It will read a forged payslip with the same confidence as a genuine one. Forensic analysis answers a different question: is this document what it claims to be?
Yes, by API. Verification sits inside the existing assessment flow and the verdict surfaces in the tool your analysts already work in.
Processing sits within a fraud-prevention basis, with every analysis logged and configurable retention periods. The architecture is designed to be compatible with GDPR, DORA and the EU AI Act.
The verdict is not binary. Anomalies are returned with their severity, which lets an analyst request an additional document rather than decline. An isolated low-severity signal is a question to ask, not a rejection.
We run a sample of your real applications together — including, if you have them, confirmed fraud cases you already identified. It is the only honest way to measure what the system actually catches on your flow. Allow around forty minutes.