Fraud detection for non-bank lenders and MCA funders

Common fraud patterns in MCA and SMB lending

Application fraud in non-bank lending typically takes several forms: fabricated or altered bank statements (inflating deposits or removing NSFs); synthetic business identity fraud (using a real EIN with a fictitious business or swapped principals); stacking fraud (obtaining advances from multiple funders simultaneously by not disclosing existing positions); and chargeback fraud (generating artificial sales through self-swipes then disputing the transactions).

Each pattern leaves signals — document metadata anomalies, deposit spikes inconsistent with business type, UCC liens not disclosed on the application, or ACH outflows to known funder descriptors. A structured underwriting workflow with documented evidence capture makes those signals visible and preserved.

How Hadrian supports fraud detection

Hadrian's AI-assisted document review can flag anomalies in submitted documents — formatting inconsistencies, round-number deposit patterns, or data that does not match stated business type. These flags surface in the case evidence graph as signals for reviewer investigation rather than automatic declines.

The evidence graph also captures outputs from third-party fraud and verification services — identity verification, KYB checks, UCC searches, bank connectivity — as structured case artifacts. When multiple signals align (identity mismatch + UCC lien not disclosed + recent revenue spike), the reviewer sees the full picture in one place.

Documentation for fraud claims and collections

If an advance defaults and fraud is suspected, the underwriting case record is the foundation for any legal claim. Funders who can produce a contemporaneous record of what documents were submitted, when, and what the underwriter reviewed — including any fraud signals that were flagged and how they were resolved — are in a materially stronger legal position than those reconstructing the file from memory.

Hadrian's tamper-evident audit ledger records the timing and content of every case event, making the record credible as evidence in collections proceedings or fraud litigation.

FAQ

Fraud Detection for Lending — common questions

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