Topic starter
11/07/2026 8:30 pm
TL;DR: AI-generated check fraud can still look routine enough to pass human review, according to Secureframe. Payment controls that depend on appearance and exception handling need stronger provenance checks and clearer approval ownership.
Editorial analysis by NHI Mgmt Group, based on content published by Secureframe: “What a Fake Check Taught Us About AI-Enabled Banking Fraud: Looking “Normal” Is No Longer Safe”.
By the numbers:
- About two-thirds of businesses encountered check fraud in 2024.
Key questions
Q: Why do Positive Pay-style controls still miss some check fraud?
A: Because they verify record matching, not the deeper question of whether the instrument was created with authorised intent.
Q: What are the warning signs that AI-enabled fraud is bypassing payment controls?
A: Frequent exceptions, routine approvals under time pressure, and forged items that look ordinary rather than suspicious are all warning signs.
Q: What breaks when AI-generated fraud looks routine enough to pass review?
A: Review processes break when they rely on human pattern recognition to separate legitimate from malicious items.
Practitioner guidance
- Tighten exception approval criteria Require second-factor validation or out-of-band confirmation for any deposited item that hits a Positive Pay exception or similar manual review queue.
- Separate payment legitimacy from visual inspection Add provenance checks that tie each check to approved issuance records, authorised payment runs, and accountable business owners.
- Reclassify paper checks as high-risk Move paper checks into a higher-friction control path even if the channel is used infrequently, because low volume does not reduce the fraud payoff.
Bottom line: AI-generated check fraud weakens the value of controls that depend on human recognition of suspicious-looking items.
Glossary terms: AI-Enabled Banking Fraud, Exception handling, Provenance, Positive Pay
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This topic was modified 2 hours ago by NHI Mgmt Group
11/07/2026 8:59 pm
AI-enabled fraud is now a trust problem, not a document-quality problem. The Secureframe case shows that believable artifacts can replace stolen access as the primary fraud enabler. Once the attacker can generate something that looks operationally normal, controls built around anomaly spotting lose their edge. For practitioners, the control question shifts to provenance and authority, not appearance.
A few things that frame the scale:
- Nearly 60% of companies reported that fraud losses were still increasing in 2025.
A question worth separating out:
Q: How should banks and finance teams respond when forged checks are accepted as routine?
A: Treat the event as a control-design failure, not just a fraudulent transaction. Tighten exception workflows, require independent confirmation for high-risk payments, and move from appearance-based review to evidence-based validation of issuance and authority.
👉 Read our full editorial: AI-enabled banking fraud is making ordinary checks unsafe