OrboGraph

Fraud isn’t slowing down—it’s accelerating. In 2025, global fraud losses hit an estimated $442 billion, with nearly 70% of adults worldwide encountering a scam, according to the GASA Global State of Scams 2025 Report.

As fraudsters grow more sophisticated, financial institutions must be ready for emerging threats like synthetic identities, deepfakes, account takeovers, money mule activity, and emotionally driven scams—risks that will only intensify in 2026 and beyond.

To stay ahead, many organizations are turning to Explainable AI (XAI). Unlike traditional “black box” models, XAI brings transparency to fraud detection by clearly showing why a transaction, item, or account is flagged. This clarity helps fraud analysts work faster, make more accurate decisions, and meet rising regulatory expectations for trust and accountability.

Read today’s blog post to learn how XAI is critical for check fraud detection.

Why Explainable AI is Critical for Fraud Defense | OrboGraph Fraud will evolve through synthetic identities, deepfakes, mules, takeovers, and emotional scams Explainable AI reveals why fraud models flag transactions Banks need XAI to meet regulations, prove fair automated decisions, and continually refine fraud defenses How large of a challenge was fraud in 2...

TechCrunch recently highlighted research showing that more than half of U.S. consumers have experienced increased fraud attempts.

Furthermore, Featurespace COO Tim Vanderham also noted just how dependent the U.S. still is on paper checks, with 11 billion deposited each year.

73% of banks now view check fraud as a major challenge, and many are investing more heavily in prevention. What stands out, however, is the shift toward treating check fraud as part of broader financial crime, especially as it overlaps with laundering, trafficking, and account-takeover schemes.

AI is becoming central to that effort. Image forensics is improving On-Us detection for stolen or altered checks, while AI-driven deposit fraud detection helps identify fake accounts, drop accounts, and money mule activity earlier in the process.

These trends signal a clear message: the institutions that continue to advance their detection strategies will be better positioned to protect their customers and reduce losses.

TechCrunch: Fraud Detection Leader Featurespace Highlights AI Check Fraud Solution | OrboGraph Yet, this ongoing trust comes with significant risks: check fraud. It's important for banks to deploy AI technology for both On-Us and Deposit fraud detection.

The payee line has one of the most targeted areas on a check by a fraudster. However, it still doesn’t get the attention it deserves from fraud prevention.

Check washing, digital “check cooking,” and line stuffing make it easy for criminals to alter payee information without disrupting the rest of the check. And, as more deposits move through mRDC and ATMs, these alterations become even harder to identify through traditional review.

Banks are taking a closer look at the payee line and strengthening how they validate it. With tools like OrboGraph’s Anywhere Payee and image forensic analysis, they can catch subtle payee-line changes including spacing, handwriting, and added names that often go unnoticed.

Bringing more awareness to this attack point is essential. Strengthening the payee field closes a long-standing gap that fraudsters continue to prey upon.

Do you think payee-line protection gets enough attention in fraud strategies today?

Explore how financial institutions are reinforcing payee-level defenses in today’s OrboNation blog post.

Payee Problems: The Overlooked Weak Point Fraudsters Love to Exploit | OrboGraph Fraudsters exploit the payee field to alter checks and avoid detection Techniques include check washing, digital editing, and adding extra payee names Banks can use AI tools to identify alterations and strengthen payee field security While check fraud tactics have drastically evolved over the past d...