What it sells
AI Document Fraud Detection for Banks, Credit Unions & Lenders
Business model breakdown
Buyer: who owns the budget?
Head of fraud / risk / account protection at the lender (testimonials from Sr. Manager Account Protection, Head of Fraud, Director Fraud Management).
Trigger: what makes them buy now?
AI-generated fake documents (site claims 1 in 16 documents show fraud signs, AI fraud up 5x) flowing into underwriting and account opening.
Workflow: what inputs become what output?
Ingest bank statements, pay stubs, IDs via API or web app, detect tampering and AI-generated fakes, score risk, add credit insights and decisioning, route to analysts.
Economics: what it saves and how it charges
Volume-based SaaS; claims $80M fraud losses prevented at BCU, 90% fewer manual reviews at BHG, 30 minutes shorter application processing at Ramp.
Distribution: how it reaches customers
Demo-led enterprise sales with a free pilot on the customer data; integrations and partners.
Funding history and coverage
| Round date | Stage | Amount | Investors | Source |
|---|---|---|---|---|
| — | Y Combinator Summer 2018 | — | Y Combinator | ycombinator.com |
Profiles summarize public reporting and company websites. Business-model notes are MaxBC's reading of that material, not confirmed facts; unknowns are left blank rather than guessed. Updated 2026-10-12.