2025 Document Fraud Report

The landscape of document fraud continues to evolve at a rapid pace, presenting both challenges and opportunities for financial institutions and fraud prevention teams. 

Our comprehensive analysis of fraud patterns throughout 2024 and early 2025 reveals a complex picture of increasingly sophisticated fraud attempts, counterbalanced by significant advances in detection capabilities. 

Most notably, 2024 saw the successful introduction of our first AI Risk Agent, the AI Fraud Analyst, which has transformed our ability to detect and prevent document fraud while significantly reducing manual review time.

Building on the 2024 report’s findings, which underscored economic downturns and rising fraud activity, this year’s report focuses on leveraging AI to mitigate fraud risks, enhance operational efficiency, and build trust in a digital-first financial landscape.

Key Findings from the 2025 Report 

• Document fraud attempts tend to be concentrated in financial documents, with bank statements comprising 59% of fraudulent documents
• Our AI Fraud Analyst averages 72 seconds per document review, compared to an average of 10 minutes for human reviewers, with notably strong performance on reducing review escalations. 
• The number of fraudulent templates detected increased by 50% from 2023 to 2024, highlighting the growing sophistication of fraud attempts
• Mid-week remains the peak period for fraud attempts, suggesting fraudsters are trying to blend in with normal business operations

The State of Document Fraud in 2025

Document fraud remains a persistent challenge in the financial services industry, with our analysis revealing that fraudsters are becoming increasingly sophisticated in their approaches. For this analysis, we reviewed millions of documents, providing unprecedented insight into fraud patterns and emerging threats.

Fake bank statements continue to be the primary target for fraudsters, accounting for 59% of all fraudulent documents detected. This preference for bank statements isn't surprising, as they serve as fundamental proof of financial stability and income for many financial products. Following bank statements, pay stubs (11.7%) and utility bills (10.2%) round out the top three most frequently manipulated document types, highlighting fraudsters' focus on documents that establish financial credibility and proof of address.

Top Fraudulent Document Types

1. Bank Statements
2. Payslips
3. Utility Bills
4. Invoices
5. Tax Forms

The geographic scope of document fraud has expanded significantly, though English-language documents still dominate the landscape. Of the fraudulent documents detected, the majority were in English, followed by French and Spanish. This distribution suggests that while fraud remains predominantly focused on English-speaking markets, fraudsters are actively operating across linguistic boundaries (predominantly from Europe in our analysis), requiring fraud detection systems to be equally sophisticated in multiple languages.

Language Distribution

1. English
2. French 
3. Spanish 
4. German 
5. Portuguese

Evolution of First-Party and Third-Party Fraud

The landscape of document fraud is becoming increasingly complex, with our analysis revealing distinct patterns in how fraudsters modify documents. Our data provides unprecedented insight into the nature of these modifications, helping us better understand the balance between first-party and third-party fraud attempts.

The most striking finding from our analysis is the prevalence of financial detail manipulation. In 46% of fraudulent documents, we found that only financial details were edited, while identity information remained unchanged – a clear indicator of first-party fraud. This suggests that a significant portion of fraud attempts come from individuals using their real identities but misrepresenting their financial situations. This pattern has shown a 4% increase from the previous year, indicating a growing trend toward first-party fraud.

Surprisingly, pure identity fraud – where only identity details are modified but financial information remains unchanged – represents a relatively small portion of fraud attempts at just 6% of cases. This suggests that traditional third-party fraud, where fraudsters solely attempt to impersonate others, is less common than often assumed.

The most concerning trend emerges in the 48% of fraudulent documents where both identity and financial details have been modified. This hybrid approach suggests a more sophisticated form of fraud, where perpetrators are manipulating multiple aspects of documents to create entirely fictional scenarios. While some of these cases represent traditional fraud attempts, the comprehensive nature of these modifications – altering both identity and financial information – could also indicate synthetic fraud schemes, where fraudsters create entirely fictional identities backed by falsified financial documentation. The high percentage of these comprehensive modifications indicates that fraudsters are taking an increasingly thorough approach to document manipulation, potentially using these extensively modified documents as part of larger synthetic identity fraud operations.

The implications of these findings are significant for fraud prevention strategies. With 94% of fraudulent documents including alterations to financial details (either alone or in combination with identity changes), financial detail verification must be a core component of any comprehensive fraud prevention strategy. This is particularly crucial given that these trends show consistent growth, with the percentage of documents containing only financial alterations increasing by 4% since last year.

These patterns also suggest a need for a more sophisticated, multi-layered approach to document verification. Organizations need to be equipped to detect both identity and financial manipulation, understanding that these often occur in tandem. The high percentage of documents with both types of modifications (48%) indicates that fraud detection systems must 

Document Manipulation Techniques

Bank statement manipulation

Our examination of bank statement fraud across major U.S. financial institutions in 2024-2025 reveals several significant patterns in both document volumes and fraud rates. This analysis spans over millions of bank statements from ten major financial institutions, providing unprecedented insight into document fraud patterns.

Most Commonly Manipulated Bank Statements

1. Huntington National Bank
2. Navy Federal Credit Union
3. Capital One
4. Truist Bank
5. PNC Bank
6. TD Bank
7. Well Fargo 
8. Regions Bank
9. Citizens Bank
10. U.S. Bank 

A striking pattern emerges when examining fraud rates across institutions. Huntington National Bank shows the highest fraud flag rate, followed by Navy Federal Credit Union and Capital One. This clustering of higher fraud rates among smaller and specialized institutions reveals important patterns in fraudster behavior.

This comprehensive analysis reveals several crucial patterns in how fraudsters target bank statements:

• Regional bank statements show surprisingly high manipulation rates
• Credit union and credit card company statements consistently show elevated manipulation rates
• Statements from larger national banks generally show lower manipulation rates despite higher volumes

See how Inscribe detects fake bank statements 

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Most common document fraud signals

Our analysis has revealed increasingly sophisticated document manipulation techniques, with five key signals emerging as the most frequent indicators of fraud:

• Font-Based Manipulation: Leading our list of fraud signals, font-related anomalies have become increasingly common. Fraudsters attempt to match fonts and maintain consistency across documents, but subtle variations in typography often reveal manipulation attempts. Even when the changes appear convincing to the human eye, these inconsistencies can be detected through advanced analysis.

• Text Editing Patterns: The second most common signal comes from edited text detection. While fraudsters are becoming more sophisticated in how they alter critical information while trying to maintain document formatting, our systems can identify signs of text manipulation even when visually imperceptible.

• Document Fingerprinting: Third on our list is document fingerprinting issues, which reveal patterns of template reuse and coordinated fraud attempts across multiple submissions. These fingerprints help identify when the same document template has been used multiple times with different information.

• X-Ray Analysis: X-ray detection ranks fourth among our top signals. This sophisticated analysis method can reveal document alterations that are invisible to the naked eye, exposing attempts to obscure or modify document information. The presence of these anomalies often indicates deeper manipulation of the document's structure.

• Template Detection: Rounding out our top five signals is template detection. As template-based fraud becomes more prevalent, our systems have become increasingly adept at identifying when standardized templates are being used to create fraudulent documents. This is particularly crucial given the 49.8% year-over-year increase in template-based fraud attempts.

Most Common Fraud Signals

1. Fonts
2. Edited Text
3. Fingerprint
4. X-Ray
5. Templates

These signals, working in concert, provide a comprehensive framework for detecting sophisticated document manipulation attempts. The evolution of these signals reflects the increasingly complex nature of document fraud and the need for advanced detection methods to stay ahead of emerging threats.

Most commonly edited document fields

Our analysis of specific field modifications provides crucial insight into fraudsters' priorities. Name fields show the highest rate of manipulation at 17.9% of fraudulent documents, closely followed by address modifications at 17.5%. This near-equal rate of name and address changes suggests a coordinated approach to identity manipulation, where fraudsters ensure consistency across multiple identifying fields.

The manipulation of temporal and financial information shows another clear pattern. Document dates are modified in 15.3% of fraudulent documents, while transaction descriptions and amounts are altered in 14.6% and 14.0% of cases respectively. This cluster of modifications around financial details reveals how fraudsters typically attempt to create a false narrative of financial health or stability. Alterations to transaction descriptions, specifically, could be to hide the source of funds or evidence of risky spending habits. 

Most Commonly Edited Fields

1. Name
2. Address
3. Document Date
4. Transaction Description
5. Transaction Amount

Document template-based fraud

The use of fraudulent templates saw a dramatic 49.8% year-over-year increase from 2023 to 2024. This surge in template-based fraud represents a significant shift in how fraudsters operate, moving from individual document manipulation to more systematic approaches using templates that can be quickly customized for different applications.

The rise in template-based fraud coincides with the increasing accessibility of AI tools and technology. As AI image generation and editing capabilities become more widespread and user-friendly, fraudsters can more easily create and modify convincing document templates at scale. What once required significant technical skill and time investment can now be accomplished more quickly and efficiently using AI-powered tools, making template-based fraud more attractive to bad actors.

Social media platforms and online forums have further accelerated this trend, providing spaces where fraudsters can share and sell these AI-enhanced templates, making sophisticated document fraud accessible to a broader audience than ever before. This democratization of fraud capabilities through AI and technology presents a growing challenge for financial institutions and underscores the need for equally sophisticated detection methods.

Time-based patterns & attack vectors

Fraudsters have developed sophisticated timing strategies to blend their activities with legitimate applications. Peak fraud attempt activity occurs during standard business hours, with Tuesday through Thursday showing the highest volumes. This timing strategy appears designed to help fraudulent applications blend in with normal business operations, making them harder to detect through timing alone.

Seasonal patterns add another layer to this story. First-party fraud shows distinct peaks during the first half of the year, particularly around tax season, with a notable dip observed in May before rising again. This pattern, which mirrors trends observed in 2022, suggests that fraudsters may be taking advantage of the increased volume of legitimate applications during tax season to blend in their fraudulent attempts.

The Impact of AI Agents in Fraud Detection

The introduction of our AI Fraud Analyst in 2024 marked a turning point in our fraud detection capabilities. With hundreds of thousands of total runs, this AI Risk Agent has demonstrated remarkable efficiency and accuracy. Our customers’ weekly usage of the AI Analyst has grown nearly fivefold since July 2024, demonstrating rapid adoption and growing confidence in its capabilities.

The AI Analyst has shown particular strength in reducing false positives and streamlining the review escalation process, reducing document review escalations (documents that fall into an uncertain or inconclusive risk rating) by 73% for all documents, including images. 

See our AI Fraud Analyst in action

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A skilled fraud analyst typically spends 5–15 minutes per document for straightforward cases, such as basic identity verification or straightforward pay stubs. For more complex cases, like reviewing business financial statements, tax documents, or cases with potential red flags, it can take 30 minutes to several hours per document.

Note that in high-volume environments like banks, lending institutions, or fintech platforms, fraud teams may batch-review documents. This can lead to faster per-document times (e.g., 3–5 minutes) due to specialization but may compromise depth for speed. Therefore, in this report, we are assuming an average of 10 minutes per document review for a human analyst.

With that in mind, the efficiency gains with our AI Fraud Analyst have been substantial, and have the potential to fundamentally transform how organizations can approach document verification. What once took a human analyst 10 minutes per document now takes just 72 seconds, representing an 88% reduction in review time. 

But the true transformation becomes apparent when processing multiple documents. Consider a typical batch of 30 documents: while a human analyst would need to spend 5 hours reviewing these sequentially, the AI Analyst can process them all in parallel in just 72 seconds. This 99.6% time savings doesn't just represent faster processing – it means fraud teams can completely reimagine their workflows. Tasks that once consumed an entire morning can now be completed before the first cup of coffee. Teams can shift from spending their days on routine document review to focusing on complex cases that require human expertise, strategic fraud prevention initiatives, and proactive risk management. 

This dramatic efficiency gain, combined with the AI Analyst's impressive 89% precision rate, means organizations can dramatically scale their document processing capabilities without sacrificing accuracy or adding headcount.

Implications & Strategic Considerations for Risk Teams

The trends and patterns identified in our analysis have far-reaching implications for the financial services industry and beyond. Here's what these findings mean for different stakeholders:

Operational implications

The efficiency gains demonstrated by the AI Fraud Analyst (72 seconds vs 217 seconds per document review) have significant implications for operational planning:

• Potential for 62% reduction in document review time
• Opportunity to reallocate skilled fraud analysts to complex cases
• Improved customer experience through faster application processing

However, organizations must balance these efficiency gains with the need for human oversight and expertise in complex cases. The optimal approach we recommend is a hybrid model where AI handles routine reviews while human analysts focus on high-risk cases and pattern recognition.

Risk management evolution

Traditional periodic review cycles, once the backbone of fraud prevention, are proving increasingly inadequate in today's rapidly evolving threat landscape. Fraudsters are now able to create and distribute new document templates faster than conventional review cycles can detect them, creating a growing vulnerability in traditional risk management frameworks. This acceleration of fraud techniques demands a more dynamic, responsive approach to document verification and risk assessment.

The integration of document forensics data into risk models has become not just beneficial but essential. Organizations that have successfully adapted to this new reality are those that have moved beyond traditional credit metrics to incorporate sophisticated document analysis into their core risk assessment processes. This integration allows for more nuanced risk assessment and better-informed lending decisions.

Perhaps most significantly, the interconnected nature of modern fraud attempts has made cross-institutional collaboration a crucial component of effective risk management. Fraudsters often test their templates across multiple institutions, making it difficult for any single organization to build a comprehensive picture of emerging fraud patterns. Forward-thinking institutions are increasingly participating in collaborative networks, sharing data and insights about new fraud patterns to collectively build stronger defenses against template-based fraud.

Summary of risk management considerations:

• Traditional periodic review cycles may be insufficient given the rapid evolution of fraud techniques
• Risk models need to incorporate document forensics data as a key input
• Cross-institutional collaboration becomes increasingly important for template detection

Regulatory and compliance considerations

The evolving sophistication of fraud attempts is creating new challenges in the regulatory compliance landscape. As financial institutions increasingly rely on automated systems and AI for fraud detection, regulators are paying closer attention to how these systems make decisions and protect consumer rights. This heightened scrutiny is reshaping how institutions approach both fraud detection and regulatory compliance.

The need for enhanced audit trails has become particularly critical in this new environment. Institutions must now be able to explain and justify their fraud detection decisions to both regulators and customers with unprecedented transparency. This requirement is pushing organizations to develop more sophisticated logging and tracking systems that can document every step of the fraud detection process.

The challenge of maintaining explainable AI models while keeping pace with sophisticated fraud attempts has emerged as a critical balancing act. We’ve found that the most successful institutions are those that have found ways to harness advanced AI capabilities while maintaining transparency in their decision-making processes. These organizations are developing new frameworks that allow them to leverage the power of AI while ensuring their methods remain auditable and explainable to regulators.

Looking ahead, we're seeing early signs that regulators may introduce new requirements around document verification processes. Forward-thinking institutions are staying ahead of these developments by implementing robust verification systems now, rather than waiting for regulatory requirements to catch up with fraud trends.

Check out our new AI Compliance Analyst beta that instantly verifies business legitimacy, analyzing key data points like Secretary of State records, web presence, and transaction patterns, all while catching risks humans may miss.

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Summary of regulatory and compliance considerations:

• Need for enhanced audit trails in automated decision systems
• Importance of maintaining explainable AI models for regulatory review
• Potential for new regulatory requirements around document verification

Industry-Wide Impacts

The ripple effects of evolving fraud patterns are fundamentally reshaping the financial services landscape. We're witnessing a transformation in how financial institutions approach customer verification and risk assessment, with implications that extend far beyond individual organizations to affect the entire industry ecosystem.

Perhaps most notably, we're seeing a fundamental shift in how institutions approach fraud prevention collaboration. The growing sophistication of fraudsters has led to the emergence of industry utilities and shared databases for fraud prevention, marking a significant departure from the traditionally siloed approach to fraud prevention. This collaborative approach is proving particularly effective in combating template-based fraud, as it allows institutions to quickly identify and respond to new fraud patterns as they emerge.

Market implications

Market evolution is being driven by several interrelated factors. Credit costs across the industry are rising as institutions invest in more advanced detection systems and absorb losses from increasingly sophisticated fraud attempts. This cost pressure is pushing institutions to accelerate their adoption of digital document verification capabilities, leading to a broader industry shift toward automated, real-time verification systems.

Competitive implications

The market is also witnessing the emergence of fraud prevention capabilities as a key competitive differentiator. Institutions that can effectively balance fraud prevention with customer experience are winning market share, particularly in digital lending and banking services. This is driving a virtuous cycle of investment in fraud prevention technology, as institutions seek to maintain or gain competitive advantage in an increasingly digital marketplace.

Looking Ahead: Recommendations for 2025

As we look to the future of fraud prevention, several key recommendations emerge from our analysis:

• The adoption of AI-powered fraud detection systems has become crucial for maintaining effective fraud prevention while managing operational costs. Organizations should prioritize implementing these systems to reduce manual review time and improve accuracy.
• Given the rise in template-based fraud, organizations need to strengthen their template detection capabilities and maintain updated databases of known fraudulent templates. The significant increase in template usage suggests this will continue to be a major vector for fraud attempts.
• Multi-language fraud detection capabilities are becoming increasingly important. While English documents remain dominant, the sophistication of fraud in other languages requires robust detection capabilities across multiple languages.
• Cross-document verification processes should be enhanced to detect coordinated fraud attempts across multiple document types. The rise in sophisticated, multi-document fraud attempts makes this particularly important.

Perhaps most notably, we're seeing a fundamental shift in how institutions approach fraud prevention collaboration. The growing sophistication of fraudsters has led to the emergence of industry utilities and shared databases for fraud prevention, marking a significant departure from the traditionally siloed approach to fraud prevention. This collaborative approach is proving particularly effective in combating template-based fraud, as it allows institutions to quickly identify and respond to new fraud patterns as they emerge.

The Path Forward in Document Fraud Prevention

As we look ahead, the document fraud landscape continues to evolve at an unprecedented pace. Our analysis reveals several critical trends that demand attention: the dramatic rise in first-party fraud, the increasing sophistication of template-based attacks, and the varying vulnerability of different financial institutions to fraud attempts. The financial services industry stands at a crucial juncture where traditional fraud prevention methods alone are no longer sufficient.

The data is clear: organizations that rely solely on identity verification or manual review processes are increasingly vulnerable to sophisticated fraud attempts. With 94% of fraudulent documents including alterations to financial details, comprehensive document verification has become essential for protecting against both first-party and third-party fraud.

Take action today! Don't let document fraud impact your bottom line. Book a personalized demo with our team to learn how AI Risk Agents can automate tedious manual review tasks and protect your business from fraud losses. 

Methodology

This report combines traditional fraud detection metrics with data from our new AI Fraud Analyst system, analyzing over millions of fraudulent documents and hundreds of thousands of AI analysis runs from 2024 and early 2025. Our analysis includes document type classification, language detection, fraud signal analysis, and template detection, providing a comprehensive view of the current fraud landscape.