2026 State of
Document Fraud
How is AI changing document fraud? We analyzed millions of documents and interviewed 15 practitioners to find out.
Documents flagged as fraudulent
Increase in AI-generated fraud (Apr–Dec 2025)
Of fraud leaders concerned about AI-enabled fraud
Introduction
Document fraud has existed as long as documents have been used to establish trust
Fraudsters have always sought to exploit the gap between what a document claims and what is actually true.
In the 1920s, Victor Lustig twice convinced scrap metal dealers to purchase the Eiffel Tower by posing as a government official. He forged ministry stationery, rented a suite at the Hôtel de Crillon to receive meetings, and presented fabricated credentials authorizing him to sell the tower for scrap. His first victim was so embarrassed at being conned that he never reported the crime, allowing Lustig to attempt the same scheme a second time.
What makes this moment different is the scale and speed at which that exploitation can happen.
When I started in fraud back in the 1990s, fraud was really pretty straightforward and simple. There were only like two types of fraud. There was credit card fraud, if you had a credit card, and there was like check fraud if you had a bank card. But what happened over the years is banks started to release more products. During that time, fraud got a lot more complicated. But the tech also improved pretty dramatically.
Today, generative AI can produce a realistic pay stub in seconds. Template marketplaces sell editable bank statements for under ten dollars. A fraudster with no technical skills can purchase, customize, and submit convincing documentation without ever touching Photoshop.
And it is working. In 2025, Inscribe flagged approximately 6% of all documents processed across our network as fraudulent. That is roughly one in sixteen documents showing signs of manipulation, fabrication, or misrepresentation.
This report synthesizes what we learned in 2025 from three sources: detection data from the Inscribe network spanning millions of documents across banks, credit unions, fintechs, and lenders; a survey of 90 fraud and risk leaders conducted in November and December 2025; and interviews with practitioners including senior underwriters, chief risk officers, fraud managers, and industry experts.
The findings point in a clear direction. Document fraud is accelerating. Manual review is reaching its limits. And organizations that adapt will pull ahead of those that do not.
But this is a evolution, not defeat. The same AI capabilities fraudsters use to create convincing fakes can be deployed to detect them. The fraud fighters we interviewed are not discouraged. They are adopting new tools, sharing intelligence across institutions, and rethinking workflows that have not changed in decades.
What follows is organized into four sections:
By the Numbers
The AI Arms Race
The Cost of Inaction
Winning Strategies
This report is designed to help fraud fighters and risk leaders understand the landscape, benchmark your approach, and identify opportunities to strengthen your defenses.
Let's start with what the data tells us.
01
By the Numbers
Every fraud strategy starts with understanding the threat. This section draws on Inscribe's year-to-date network data spanning millions of documents processed across hundreds of financial institutions, combined with survey responses from 90 fraud and risk leaders.
Together, they reveal where document fraud is concentrated, which document types carry the highest risk, and where fraudsters are finding soft spots in verification workflows.
The scale and shape of document fraud in 2025
Across our network in 2025, approximately 6% of all documents processed were flagged as fraudulent. That translates to roughly one in sixteen documents showing indicators of manipulation, fabrication, or misrepresentation.
To put that in context: if your organization processes 10,000 loan applications per year and each application includes three documents, you are potentially looking at over 1,800 fraudulent documents annually. Some will be obvious. Many will not.
For investigators who work these cases daily, the volume is relentless. Las Vegas Financial Crimes Detective Marc Evans Mark Evans spent six years in financial crimes before moving to cyber crimes, and document fraud was a constant.
I would see document fraud every single day. I'm talking about fake DMV temporary passes, DMV titles, fake treasury bonds that were completely made from scratch. I actually caught a guy one time—when we got him in custody, he was in the middle of making a fake treasury check, and it was still up on his computer screen in Photoshop.
The cases Evans describes are not outliers. They reflect the industrialization of document fraud that our network data confirms.
The documents are just so much better looking now than they used to be. You can't necessarily tell if the spacing is off because thanks to AI and some of these other tools, the documents are just so much better looking now.
— Timothy O'Rear, Senior Underwriter, Rapid Finance
This tracks with what we have heard across the industry. Frank McKenna, Chief Fraud Strategist at Point Predictive and author of the Frank on Fraud blog, has watched fraud evolve for three decades. He describes fraudsters as increasingly sophisticated in exploiting multiple channels and document types simultaneously.
As document quality improves, the challenge shifts from just a detection problem to an operational burden. When fraudulent and legitimate documents are indistinguishable at first glance, review teams face higher workloads, longer queues, and increased risk of both missed fraud and unnecessary friction for good customers.
Fraud pressure is broadly distributed across document types
When we examine fraud rates by document type, a clear pattern emerges: most documents used to verify critical facts exhibit a similar baseline fraud rate, generally in the 4–7% range.
Bank statements, pay stubs, tax forms, business filings, and other financial documents are all consistently targeted. This indicates that document fraud is not confined to a single document class, nor driven by one specific verification step. Instead, fraud pressure is broadly distributed across workflows wherever documents are used to establish trust.
This aligns closely with how fraud and risk leaders view the landscape. In our survey, respondents consistently cited core financial and income-related documents as the most vulnerable to manipulation (reflecting the high-stakes decisions those documents support).
One document type breaks from the baseline: utility bills, which show a significantly higher fraud rate than other document categories in Inscribe’s network.
This does not mean utility bills are uniquely dangerous. Instead, they sit at the intersection of identity, convenience, and perception.
Utility bills are commonly used to verify proof of address, often as a secondary or supporting document. Because an address underpins many downstream decisions, from account opening to credit approval, utility bills frequently serve as an early anchor in identity formation.
At the same time, altering a utility bill often feels less serious than altering a primary financial document, even though the legal and risk implications are the same. In many cases, the behavior is not overtly malicious. Applicants may have recently moved, lack updated documentation, or “fix” an address without recognizing the action as deception.
The intent varies, but the risk signal does not.
This distinction matters. Treating utility bills as low-risk or secondary documents can create blind spots in verification workflows.
Applying consistent scrutiny across all documents used to verify critical facts helps protect institutions and customers, especially when individuals may unknowingly cross a line.
Bank statements top the list of fraud fighter concern
Our survey of 90 fraud and risk leaders asked which document types they believe are most vulnerable to manipulation.
In our survey, 85.56% of respondents cited bank statements as the document type they are most concerned about, the highest of any category.
Bank statements, especially, are very complicated because we had to parse transactions. We had developed a system in terms of checking, ‘Do you see a transaction from Fleetio?’ which is software that our legitimate customers use.” That was our way of analyzing these bank statements from a fraud and credit perspective. It was heavily manual, everything used to take an hour.
— Anurag Puranik, Chief Risk Officer, Coast
The challenge with bank statements is their complexity. Unlike a pay stub with a handful of fields, a bank statement contains dozens of transactions, running balances, dates, and formatting elements. That complexity creates more opportunities for subtle manipulation and more work for reviewers trying to catch it.
Pay stubs and business financial documents rank second and third, reflecting the income verification use case that drives much of document fraud in lending.
Detection challenges are multi-dimensional
Our survey asked fraud leaders about their biggest challenges in detecting document fraud. The results reveal a problem that spans technology, process, and resources.
The top challenge, cited by 72% of respondents, is detecting subtle AI-driven changes. Angela Diaz sees this as a shift that requires fraud teams to think differently about quality improvements that may not be visible.
Five years ago, ten years ago, you could spot a fake passport or a fake driver's license that was sent in. It looked so digitally perfect that you're like, get out of here. Or it was a bad Photoshop job. I think a lot of the phishing emails and phishing texts were easy to spot five years ago, ten years ago. There were major quality issues, grammar, spelling errors, just low quality logos. But we're going to see that quality go up with AI.
— Angela Diaz, Senior Principal of Operational Risk Management, Discover
The second and third challenges, time-consuming manual review (63%) and limited visibility into edits (62%), are related. Manual review takes time precisely because reviewers lack visibility into what has been changed. They are looking for anomalies without knowing where to look.
Notably, only 13% of respondents said they are unsure what signals to look for. Fraud teams know what they are looking for. The problem is that the signals are increasingly invisible to manual inspection.
By the Numbers
Document fraud operates at scale
With approximately 6% of documents flagged across Inscribe’s network, fraud is a volume and operational challenge, not an edge case.
Fraud pressure is widespread
Most document types used in verification show similar baseline fraud rates, underscoring that no single document class is immune.
Utility bills warrant attention
Higher fraud rates reflect their role in identity verification and lower perceived severity — not inherently higher risk.
Intent is not binary
Many instances of document manipulation are non-malicious, but they still introduce real risk into decisioning systems.
Consistency matters
Applying uniform verification standards across document types reduces blind spots and improves outcomes for institutions and customers alike.
In the next section, we explore how generative AI is accelerating document fraud and what that means for detection strategies.
02
The AI Arms Race
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03
The Cost of Inaction
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04
Winning Strategies
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