Loan & credit fraud detection

Fake income documents are detected through forensic analysis rather than visual review: examining files for editing artifacts, font and layout inconsistencies, and signs of AI generation that persist even in convincing forgeries. Detection also compares documents across applications, since fraud rings reuse templates with only the numbers changed. ScamAI runs these checks through a REST API inside the origination flow, returning a per-document tamper result with evidence, so underwriters see which files need scrutiny instead of manually inspecting every PDF.

In lending, synthetic identity fraud means a borrower who never existed: a blend of real and fabricated attributes, supported by forged pay stubs and statements, that passes underwriting, then defaults with no one to collect from. TransUnion measured U.S. lender exposure at $3.1B in a single year. Because the applicant cannot appear in person, the fraud depends entirely on media — a rendered face at verification and doctored documents — and both are detectable at origination, before the loan funds.

Application fraud is most detectable at origination, because that is when the fabricated material is submitted at once: the selfie, the ID, and the income documents. Screening each for manipulation at upload catches the fraud while the decision can still change cheaply — a decline or a step-up rather than a charge-off. ScamAI returns results in real time inside the approval flow, and batch endpoints let lenders rescan existing books for synthetic applicants approved before detection was in place.