
Challenges
As part of a growing credit union serving members across the U.S., BCU’s investigators were seeing more sophisticated document fraud than ever before. The team was tasked with reviewing loan documents, ACH transfers, and member account applications – each carrying the potential for hidden manipulation.
Over time, they noticed patterns: recycled documents, forged templates, and even subtle digital alterations that were difficult to detect with manual review. The team needed a way to confirm authenticity without adding hours of manual review to their workload.
“Before Inscribe, it was tough to catch every altered document just by looking at it. Fraudsters are getting smarter, and we needed something that could see what we couldn’t.”
Solution
With Inscribe’s agentic AI for document fraud detection (powered by network, forensic, semantic, and perceptual detectors) BCU can automatically surface and act on hidden fraud signals across both documents and applicants. These detectors work together to reveal deep, cross-source insights, using tools like X-Ray, fingerprint analysis, and relational links to expose patterns and connections that would be nearly impossible to identify manually.
In one case, BCU used X-Ray to connect multiple members tied to the same address. The analysis revealed that a document provided during review was originally owned by a blacklisted member with roughly $100,000 in charged-off loans – allowing the team to shut down the scheme and blacklist related accounts before further losses occurred.
The team also leveraged Inscribe to detect synthetic identities and reused document-store templates.
“In one case, a $75,000 auto loan was prevented after a bank statement was flagged for a fingerprint mismatch. That one signal changed the whole outcome of the case.”
Result
Using Inscribe’s advanced detection capabilities, the team has identified and disrupted several major schemes: a Florida loan fraud ring operating for years, a California bust-out attempt revealed by X-Ray flags on both a pay stub and a utility bill, and rising cases of template-based proof-of-income documents that are now easier to detect and stop before funding.
“Some of our largest dollar preventions in the past few years have come directly from Inscribe detections. We’re talking millions in losses prevented, and that’s made a measurable difference in how fast and how confidently we can stop fraud.”
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