The 8 types of document fraud: Ultimate guide
Fraud is fraud, right?
Well, when it comes to the types of document fraud, it’s not so cut and dry: there are different ways of faking documents which vary based on the methods employed to make and use them.
For the 8 types of document fraud, each method requires a different techniques to weed it out, requiring in-depth understanding and expertise of how the fraud happens and how to prevent it.
To implement the best anti-fraud processes for your business’s unique challenges, let’s break down the fraud techniques you're up against.
What is document fraud?
Document fraud is the act of creating, altering, falsifying documents, or using fake documents to deceive others for personal, financial, or criminal gain.
Forged documents — like utility bills, bank statements, or certificates — can unlock access to money, services, or systems otherwise inaccessible to fraudsters.
Even a single fake document can bypass critical controls, enabling crimes like identity theft, financial fraud, or regulatory evasion, and putting businesses, governments, and individuals at serious risk.
Learn more about document fraud, its history, and how to prevent it in our document fraud ultimate guide.
Why are there types of document fraud?
What’s the difference between the types of document fraud and why should you care?
The primary difference lies in how the documents are created or attained. While some forgeries involve completely fabricated documents generated from templates or AI tools, others rely on altering real documents or misusing legitimate credentials obtained through data breaches or social engineering.
It’s important for institutions to understand these discrepancies because each type of fraud carries different risks, requires different detection methods, and exposes vulnerabilities at different points in the verification process.
A one-size-fits-all approach won’t work. Organizations need comprehensive, future-proof defenses that take all variables into account — effective fraud prevention depends on recognizing how the threat is built.
Document forgery
Document forgery is the act of creating a fake document from scratch, imitating a genuine one.
History
Document forgery is as old as documents themselves, but for most of this time it was largely confined to an underground industry where success required a skilled artistic sense, physical access to legitimate examples, and specialized supplies like presses, printers, paper, and plastic.
Given this relatively high bar for creation, bad fakes were fairly easy to spot with a well-trained eye, and good fakes were costly and therefore uncommon.
The threat today
With the increasing sophistication of technology document forgery is booming. The only required supplies are a computer and free software, while digital examples of what the real deal looks like can be obtained on the internet, in your email, or through your nearest scanner.
This means that document forgery is becoming easier and vastly more common: anyone anywhere can whip up a document and try to pass it off as something it’s not.
As image creation and editing software becomes more sophisticated and fraudsters have more time to hone their skills, it's becoming harder to distinguish between genuine and forged documents.
Tools used by fraudsters
When it comes to document forgery, fraudsters have an expanding toolkit at their disposal.
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Basic forgeries. Use graphic design software like Photoshop or free online editors to alter names, dates, or values on real documents.
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More advanced. Fraudsters use document generators and template farms that provide editable versions of official-looking forms.
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Generative AI. Tools are being used to create entirely synthetic elements like ID photos, signatures, or paper textures, making fake documents harder to detect with the naked eye.
Detection methods
Just because making fraudulent documents is easier nowadays, it doesn’t necessarily mean that making good fake documents is easy.
Fraudsters usually give themselves away with typos, unprofessional formatting, and use of fonts and logos that don't match the genuine article.
Usually, a forged document made entirely from scratch can be distinguished just by comparing it to a document that's been confirmed as authentic.
Document alteration
Unlike document forgery where a completely fake document is created from scratch, document alteration or document manipulation involves making changes to an existing genuine document.
This can be as simple as writing in new dates, names, or numbers on a document, or it can take the form of professional Photoshop jobs that turn out super-convincing fake ID cards.
History
Document alteration has existed for centuries, but it was far less scalable in the era of physical paperwork. Altering a physical document often required access to specialized tools, expert craftsmanship, and significant risk, as any tampering could leave visible signs like smudges, mismatched ink, White-Out stains, or physical inconsistencies.
The threat today
With the rise of digital documents alteration became much easier: fraudsters can now edit PDFs, screenshots, or scans using simple software to change numbers, names, or dates without triggering suspicion.
What was once a slow, manual crime has become fast, repeatable, and much harder to detect — especially when the altered document still appears "real."
Tools used by fraudsters
For document alteration, fraudsters rely on precision tools that let them tweak just enough to pass undetected.
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PDF editors. Tools like like Adobe Acrobat Pro, PDF-XChange Editor, or even browser-based tools allow editing text, swapping numbers, or replacing images without disturbing the overall layout.
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Image editing programs. GIMP, Inpaint and other similar tools are used to clone backgrounds and remove traces of tampering.
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Font-matching tools. Help replicate the original typeface down to spacing and weight.
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Metadata scrubbers and masking tools. Erase footprints, hide layers, or make edits invisible to standard software — giving altered documents a clean digital fingerprint.
Detection methods
Compared to document forgery, document alteration escalates the challenge to fraud risk management teams in two main ways:
Document manipulation lets fraudsters leave most of the original file intact.
Most of the information on the provided document, therefore, may well be valid.
Fraudsters seeking to commit first-party fraud, for example, might use their real name, real phone number, real photo, and so on, but they may “adjust” their address to access a service in a country where they aren’t legally allowed to do so.
With the majority of personally identifiable information otherwise easy to verify, it’s more likely that the falsified details will be overlooked or simply assumed to be true.
Document alteration has the ability to make changes so subtle they're almost invisible to the naked eye.
Thanks to image editing programs that allow users to make pixel-by-pixel changes, document manipulations today may be tiny and imperceptible — a needle in a digital haystack.
Imagine, for example, how a bad actor might use the grade-school trick of changing a 3 to an 8. PDF editing software allows for perfect editing, leaving only traces of alteration in the document's metadata.
Manual reviews are unlikely to notice such a minute change, not due to negligence but rather simple human limitations.
This is often amplified when a review process relies on speedy checks on high volumes of applications, as one of our clients discovered during their digital mortgage underwriting process.
It’s these tiny or even invisible-to-the-naked-eye manipulations in particular that make AI document fraud detection checks a necessity.
Simply put, AI and other machine learning techniques pick up on even the tiniest alterations, and can rapidly scan a file's metadata for what changes where made and how.
Identity theft or stolen documents
Identity fraud and identity theft are often used interchangeably, and both refer to the act of obtaining someone else's personal information without their knowledge or consent to use for fraudulent purposes.
History
Identity theft has been around as long as identity itself — from impersonating nobles in ancient times to forging travel papers in wartime Europe.
In the pre-digital era, stolen documents like passports, licenses, or birth certificates were physically taken, altered, or counterfeited, but their reach was limited by geography and effort.
The threat today
The digital age changed everything: data breaches, phishing, and leaked databases now supply criminals with massive volumes of real and usable personal information.
What was once a localized, one-off crime has evolved into a global, industrialized threat — fueled by automation, anonymity, and access to stolen data.
Tools used by fraudsters
Unfortunately, identity theft thrives due to the many vectors through which fraudsters can obtain personally identifiable information (PII).
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In-person/physical. On the low-tech end, an online banking password and username combination can be picked up simply by looking over a victim's shoulder as they type, or by carefully overhearing a sensitive conversation.
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Hacking and data leaks. More technically skilled bad actors can hack into centralized online services to collect reams of well-organized personal data on thousands of customers at once, from names and addresses to credit card and Social Security numbers.
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Internet marketplaces. Online repositories that sell this information to criminals who specialize in creating and using this info in the form of fraudulent documents.
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Other methods. Phishing and social engineering, for example, involve fraudsters who take on the role of a trusted figure—a boss, a government agency, etc.—to collect usable data from unsuspecting victims.
Since thousands of individuals can be involved in these sorts of data breaches, fraudsters who get their hands on tranches of info can carry out industrial-level "serial fraud", which we'll elaborate on shortly.
Detection methods
Identity theft and the use of stolen documents are among the hardest types of fraud to detect — not because the documents are fake, but because they’re often real.
Fraudsters using stolen personally identifiable information (PII) can pass traditional verification checks with ease by presenting legitimate data.
While stolen PII is sometimes paired with document forgery or manipulation, it doesn’t have to be; even an unaltered document can be used to impersonate someone and commit fraud.
This is where conventional methods like database lookups or ID scans fall short — they validate the data, but not the context.
To detect identity theft, systems must go beyond the document itself and assess behavioral, device, and contextual signals. Advanced forensic techniques can identify red-flag patterns such as:
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Cross-referencing location metadata.
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Identifying reused or recycled documents across multiple accounts.
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Analyzing document submission patterns.
For individuals, prevention is still the best defense. Monitoring your credit, securing personal data, and being cautious about what information you share online can reduce your exposure.
Synthetic identities
Synthetic identity fraud, sometimes simply called synthetic fraud, is a newer form of identity fraud. It involves combining real information or real and fake information in a way that creates an entirely new but overall fictitious identity.
History
Synthetic identity fraud emerged in the early 2000s as a response to tightening verification controls around traditional identity theft. By combining real and fake information (for example, pairing a valid Social Security Number with a fabricated name and address), they could fool these legacy systems.
The threat today
The rise of digital onboarding and automated decisioning in financial services only accelerated the problem, making it easier to create, nurture, and scale fake identities without human oversight.
Today, synthetic identity fraud is one of the fastest-growing financial crimes globally, often evading detection by blending in with real customers.
Tools used by fraudsters
Fraudsters creating synthetic identities use a mix of social engineering, automation, and advanced technical tools to build and deploy convincing fake personas.
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Dark web marketplaces and data breach dumps. Obtaining real PII via criminal distributors, often targeting minors, the deceased, or individuals with thin credit files.
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Bots, credential stuffing tools, and fake identity generators. To automate identity creation and testing, attackers rely on software like Sentry MBA to populate forms with plausible combinations of names, addresses, demographic data, etc.
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Custom scripts or browser automation frameworks. Simulate real user behavior, bypass velocity checks, and mimic device fingerprinting patterns.
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Virtual machines and anti-detection browsers. Tools like Multilogin and Fraudfox can spoof geolocation, time zone, and hardware signatures.
Detection methods
Detecting synthetic identity fraud requires an understanding of fraudster behavior. Institutions must understand the two most common possibilities, incomplete theft and mix and matching.
Incomplete theft
A fraudster steals or buy a large batch of PII, such as photos of ID cards and credit card information, that contains an incomplete set of information for full identity theft.
On their own these pieces of information may not be enough to, say, open an online bank account, but document forgery or manipulation can easily fill in the gaps to provide additional documents. For example, a fake proof of address.
Mixing and matching
A fraudster acquires a particularly detailed set of stolen information, more than enough to successfully open an account in any one individual's name.
Without the need to forge documents, the fraudster might instead mix and match genuine information many times over—one victim's name with another's address with yet another's credit card number, and so on.
This gives fraudsters the ability to open an essentially unlimited number of unique accounts, or at least grants them an unlimited number of attempts to bypass security screenings, maximizing their payoff and/or slowing how quickly a victim realizes their identity has been stolen.
How to detect synthetic identity fraud
Fraud detection systems can be easily caught off guard by this technique: if information is properly formatted and individually valid, many systems may not flag it as problematic.
At most, a system might just get confused and forward the case for manual review, which is slower, costlier, and not without its own ability to be fooled.
It's vital to step beyond simply what a document says on the page and take a holistic view, looking at all the documents submitted on a case by case basis and against and all document verification over time.
In addition to catching hard-to-recognize fake documents, patterns of reused documents, reused information, or information that is blended in a predictable way can reveal synthetic identity fraud. This large-scale pattern recognition is a particular strength of AI fraud detection software.
Template fraud
While technically a form of document alteration, template fraud, the act of altering online-available templates of official documents to commit fraud, is so prevalent and overwhelming to first-line controls that we consider it a unique and notable brand of fraud.
History
This tactic gained traction in the last few years as fraudsters realized they could mass-produce authentic-looking statements, licenses, and certificates without needing access to real documents belonging to an individual.
The barriers to entry have dropped — anyone with internet access can download or upload convincing templates on low-profile forums and marketplaces.
The rise of privacy tools like VPNs and anonymous messaging/payment systems (like Telegram) has also made it easier to host and operate these “template farms” with little risk of being traced or shut down.
At the same time, institutions are overwhelmed with document volume and increasingly rely on automated checks, making it easier for templated forgeries to slip through undetected.
The threat today
Over 180K templates across 50+ categories are sold online, and the number continues to grow.
Similar to how identity thieves often distribute the info they've obtained through marketplaces, it's very easy to find ready-to-edit templates for all kinds of common document types online from specialized template farms or document mills.
Easily found and sold on over 100 websites, these can pop up in just a few seconds on your favorite search engine. They're available for download in PDF and image formats for a fee or even free of charge.
Usually, they're accompanied by instructions for how the end user should input their desired information and, occasionally, the fraudulent documents are validated/reviewed by their consumers, detailing what document tampering worked and which systems they breached.
Watch our previous webinar "From template farm to serial document fraud," to learn how to protect your organization from the growing threat of fake templates infiltrating the market.
Tools used by fraudsters
Fraudsters involved in template fraud rely on a combination of anonymizing tools, underground networks, and accessible design software to mass-produce believable fake documents.
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VPNs and anti-detection browsers. Help mask digital fingerprints while accessing restricted platforms or hosting their own template marketplaces.
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Telegram. Many use it as their primary distribution channel (both for selling pre-made templates and coordinating requests for specific document types) because its encryption and decentralized moderation make it difficult to monitor or shut down.
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Data breaches, phishing campaigns, and customer onboarding leaks. Used to source the stolen templates.
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Image editors. Adobe Illustrator, Photoshop, Canva, or Figma used to recreate logos, fonts, seals, and layouts with a high degree of visual accuracy.
Detection methods
Detecting template fraud requires more than checking for obvious signs of manipulation — it demands an understanding of the source material itself.
Because these documents are often built from high-quality, reusable templates, they can pass visual inspection and basic format validation with ease.
The most effective way to catch them is to compare incoming documents against known fraudulent templates, but that requires access to the templates themselves.
At Resistant AI, we specialize in this approach. Our Threat Intel Unit continuously monitors and collects data from known template farms, fraud forums, and Telegram groups.
We’ve built a growing repository of fake document layouts, structures, and visual patterns.
This database allows our AI to flag documents that match or resemble those circulating in the fraud ecosystem — even if the content has been customized.
Pre-digital document modification
Pre-digital document fraud is when a document is forged or altered, printed, then photographed or scanned to produce a new digital file.
This is an attempt to wipe away the "fingerprints" left in image or PDF metadata when a file is opened and re-saved in an editing program.
History
Before the rise of digital editing tools, document fraud was a manual craft.
Fraudsters physically altered printed documents using methods like white-out, razor blades, cut-and-paste collages, or typewriter overlays to change names, dates, and figures.
These modified papers were then retyped, re-copied, or photocopied repeatedly to mask signs of tampering — a primitive form of obfuscation that relied on degradation to hide flaws.
With the rise of desktop publishing in the 1980s and early scanning technology in the 1990s, fraudsters began incorporating photocopiers and scanners into their process, creating forged documents that could be distributed more easily.
The threat today
Today, some fraudsters still use these techniques but to submit documents digitally since physical submissions are becoming less and less common. They print a document, commit document tampering, then photograph or scan it again to submit digitally.
Alternatively, they may make the alterations before printing then reupload to wipe metadata and digital fingerprints, echoing the same analog-to-digital obfuscation strategy that has been evolving for decades.
Tools used by fraudsters
Fraudsters using pre-digital document modification techniques rely on a mix of physical tools and basic technology to forge or alter documents before digitizing them. These methods are low-tech but still effective when combined with scanning or photography to erase signs of tampering.
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Physical alteration tools. Items like scissors, glue, tape, razor blades, correction fluid, or typewriters are used to manually change printed documents.
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Photocopiers. Used to obscure physical edits by creating a “clean” version that hides visible cut lines or ink inconsistencies.
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Scanners. Capture altered documents into digital format while stripping most metadata and editing fingerprints.
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Cameras or smartphones. Often used to photograph documents instead of scanning, producing lower-quality images that mask forensic clues.
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Printers. Used to produce altered documents for rescanning or to give forged content a more “original” appearance.
Detection methods
This seemingly simple workaround commonly fools humans and less sophisticated fraud detection solutions.
Manual reviewers, for instance, may be presented with an image that looks like any other photo or scan (but aren't) while technological programs can be overly reliant on certain portions of metadata and will sign off that the file hasn't been altered even when it clearly has.
Examining documents holistically rather than in isolation is, again, vital.
Patterns such as where an image was created, the device used to create it, or even similarities in images themselves (i.e., the same lighting/filter) can be indicators that a file is coming from an untrustworthy source.
This presents an especially significant impediment for serial fraudsters, who might have passable pre-digital modifications but regularly upload from a specific place using the same equipment.
Generated document fraud
Generated document fraud uses consumer artificial intelligence, GenAI, and LLMs (ChatGPT, Midjourney, etc) to produce original documents from scratch — just describe what you want in a prompt and in seconds you can generate almost any document.
History
Generated document fraud is one of the newest and fastest-evolving forms of document manipulation, emerging alongside the rapid advancement of generative AI.
While rudimentary attempts to fabricate document elements with image editing software date back to the early 2010s, it wasn’t until 2022, with the public release of ChatGPT, that generative AI tools became widely accessible.
In 2023, the landscape shifted further as models like DALL·E, Midjourney, and Stable Diffusion began enabling image generation at photorealistic quality — and tools like ChatGPT integrated image creation capabilities.
This allowed fraudsters to generate individual document components — such as ID photos, fake signatures, seals, or even convincing paper textures — from scratch, without relying on stolen documents or existing templates.
The threat today
The result is a new wave of synthetic documents that are increasingly difficult to trace, with fraud tactics evolving as quickly as the underlying AI models themselves. Institutions are still catching up, as this threat is not only novel, but also highly scalable and continuously improving.
However, the quality of results does vary significantly. They can range from nonsense words on documents from nonexistent countries to eerily realistic.
Tools used by fraudsters
Fraudsters falsifying documents rely on a growing ecosystem of generative AI tools and support software to fabricate realistic-looking documents from scratch.
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ChatGPT. Can generate realistic-sounding text for supporting documents like letters, invoices, or certificates. Can also generate images based off sample documents.
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DALL·E, Midjourney, and Stable Diffusion. AI image generators capable of creating visual elements such as ID headshots, logos, stamps, signatures, watermarks, backgrounds that mimic paper texture and lighting, or entire document templates.
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ControlNet and img2img features (used with Stable Diffusion) – Allow for finer control and editing of document-like layouts to make them appear more realistic or match specific formats.
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Inpainting tools – Used to modify parts of an image seamlessly, such as changing a name or number without disrupting the surrounding design.
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PDF editors and layout software – Tools like Adobe Acrobat, Canva, or InDesign are often used to assemble the final output by placing AI-generated components into convincing document layouts.
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Metadata scrubbers – Remove identifying markers from image files and PDFs to conceal the document’s digital origin.
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Anonymity tools – VPNs, Tor browsers, and burner emails are used to access and distribute false documentation while hiding the fraudster’s identity.
Detection methods
As we described in our deep dive into "FraudGPT", traditional fraud checks struggle to reliably identify the minute details that expose high-quality generated documents and images as fakes.
Fighting this recent flood of generated documents instead requires multiple layers of self-reinforcing checks that are deployed consistently and simultaneously to validate everything from metadata to how an uploaded document fits into the context of other information provided by customers.
At Resistant AI, we realize that GenAI isn't a new phenomenon.
Our new ensemble of GenAI detectors identify if an image bears telltale signs of the latest AI generation techniques, analyzing both the visual textures and structural patterns of documents, and flagging anomalies with an FP (false positive) rate under 1%.
Serial fraud
Serial fraud deploys one or more of the fraud techniques above on a repeated, mass scale. Fraudsters identify a vulnerability financial institution's document controls and then use automation and other technologies to exploit that vulnerability at an industrial level.
A serial fraud example from Brazil
History
Serial fraud predates the internet, though it was once far more labor-intensive. In the pre-digital era, fraudsters relied on typewriters, photocopiers, and snail mail to submit falsified paperwork to banks, governments, or insurers, often under multiple aliases.
One notorious example is Frank Abagnale Jr., who in the 1960s posed as an airline pilot, doctor, and lawyer while forging checks across more than 20 countries, exploiting slow paper-based verification processes to stay ahead of the law.
The threat today
The combination of freely available personal information, easy-to-use forgery technology, and the semi-secrecy of online services already present several small opportunities while automation has made this type of fraud not only possible but a particularly pernicious and repeated threat among fintechs and financial services providers today.
Fraudsters submit hundreds or thousands of applications using synthetic identities or reused documents, often automated and anonymized. What once took months and a suitcase full of forged papers can now be executed from a laptop in minutes.
Tools used by fraudsters
Modern serial fraud is powered by automation and anonymity. Their tools are designed to scale, evade detection, and exploit weak verification processes across digital platforms.
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Form-filling bots. Tools like Selenium, Puppeteer, or purpose-built fraud bots automate the submission of fake applications to banks, lenders, fintech platforms, and crypto exchanges.
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Synthetic identities. Fraudsters use software or scripts to mass-produce unique but believable identity profiles, mixing real and fake data to avoid duplication flags.
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Document alteration. Slight modifications to the same false documentation using tools like Photoshop scripts, batch image processors, or automated text layer replacements, allowing hundreds of unique versions to be generated from a single template.
Detection methods
A fraudster may hide behind the safety of their computer screen, methodically testing combinations of stolen and/or forged ID cards and supporting documents in an attempt to bypass an online bank's KYC onboarding process.
Once they hit on a winning combination, they can mix and match other stolen information, reproduce endless supporting documentation, and even use generative AI to defeat liveness checks.
Enterprising criminals have even been known to write scripts to automate questionnaires, filling in fields and checking boxes in seconds.
The result can be dozens or even hundreds of fraudulent accounts under the control of a single fraudster, able to be used over and over again for scams or money laundering operations — even if one account is shut down, another is ready to take its place.
Watch how template farms contribute to serial document fraud and how we detect these repetitive templates in real-time.
This sounds elaborate, but it's not theoretical: it was the very real challenge we confronted in one major international payment processor.
It's the combination of techniques used in conjunction with one another in order to flood and overwhelm traditional fraud detection systems that makes serial fraud one of the most significant threats to understand in today's fraud environment.
Modern document fraud risk assessments must therefore call for multiple interlocking and mutually reinforcing layers of fraud detection and prevention, starting with identifying individual examples of forgery or alteration and building up a picture of fraud and abuse patterns across an entire customer base over time.
Conclusion
Taking a broad and combative view on fighting fraud does not rely on luck or intimate familiarity with specific document types.
The only way to effectively counter the individual bad actors and recurrent attempts that have come to characterize document fraud in the digital age is to adopt a document agnostic, AI-powered approach to fraud detection.
Resistant AI’s document forensics technology is built to meet this challenge head-on, analyzing every layer of a submitted document against a constantly updated fraud intelligence database and spotting patterns that humans and rule-based systems miss.
If your organization handles documents in any part of its risk, compliance, or onboarding workflows, scroll down to book a demo today to see how our AI can help you detect fraud before it costs you.
Frequently asked questions (FAQ)
Hungry for more types of document fraud content? Hear are some of the most frequently asked questions about the types of document fraud from around the web.
What is an example of document fraud?
Verto, a cross-border payments platform that faced a surge in fraudulent onboarding attempts, is an excellent example of serial document fraud.
Fraudsters submitted fake bank statements and identity documents (some entirely fabricated, others subtly manipulated) to create illegitimate accounts.
These documents often passed visual inspection but were identified as fraudulent through our AI-based document forensics, which detected reused templates and inconsistencies invisible to the human eye.
Resistant AI helped Verto detect and stop over 800 fraudulent applications in just six months, significantly reducing risk and manual review time.
What are the three levels of fraud?
Conceptually, fraud doesn’t happen in a vacuum — it typically arises when three key conditions are present in an environment.
This framework is known as the Fraud Triangle, established by the Association of Government Accountants (AGA), and it helps organizations understand the root causes of fraudulent behavior.
It consists of the following three levels:
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Opportunity. The individual sees a chance to commit fraud without getting caught. This could be due to weak oversight, lack of controls, or poorly monitored processes.
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Pressure (or Incentive). The person feels financial or personal pressure that motivates them to commit fraud. This could stem from debt, unrealistic performance targets, or personal hardship.
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Rationalization. The individual justifies their fraudulent behavior to themselves — believing it's harmless, deserved, or a temporary solution.
By recognizing and addressing each of these levels, institutions can design systems that reduce opportunity, alleviate undue pressure, and discourage rationalization through training, transparency, and accountability.
What are the two most commonly forged documents?
As a fraud detection company that deals with a variety of different segments from all over the world, we see bank documents and commercial documents (invoices, receipts, purchase orders, etc.) the most... but that doesn't mean they're the most risky.
The "riskiest" documents we encounter are those containing company information and tax details, however risk doesn't always equal fraud or forgery.
It's difficult to put a definitive label on "what documents are more often forged." Different systems collect different volumes of documents, encountering different types of fraud, not to mention unnoticed forgeries and false positives.
Unlike our competitors, we won't try to wow you with an unjustified sexy number or label. Instead, we'll point you to real, definitive, and actionable information.
For example, thanks to our Threat Intelligence Unit, we can tell you which documents have the most readily available fraudulent templates online: bank statements and IDs.
What are the five elements of fraud?
Thanks to the MBM law firm, we have an established criteria for determining whether someone committed fraud in a court of law.
In order to be convicted of fraud, a prosecutor must prove the following:
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You provided false information as a true fact.
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You knew the information your provided was false.
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Your intention was to deceive your victim.
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The affected party was relying on your statement.
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They suffered damage as a result of the falsehood.
For affected institutions, you will need to provide evidence of the following five points to effectively and legally reprimand your fraudster.
What are the two types of documents?
Documents generally fall into two categories: digital and non-digital (or physical).
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Digital documents are files created and stored electronically, such as PDFs, Word documents, or scanned images. They often contain metadata and structural markers that can be analyzed for signs of tampering.
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Non-digital documents are physical papers, like printed forms, handwritten letters, or official certificates. These can be altered manually and are typically digitized later via scanning or photography.
What is the hardest type of document fraud to detect?
We cover this in depth in our Ultimate Guide to Document Fraud, but here are three of the most challenging types and why they’re so hard to catch:
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Identity theft and stolen documents. These often involve legitimate documents used by the wrong person, making them especially difficult to flag without secondary verification or behavioral signals.
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Pre-digital document modification. When a document is printed, altered, and re-scanned, visual clues are erased, making even sophisticated detection systems struggle to differentiate it from a genuine digital original.
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Template and generative AI fraud. Constantly evolving layouts, backgrounds, and components make this type of fraud highly scalable and hard to detect without access to known fraud templates or broader submission patterns.
What is the difference between document fraud and identity theft?
Document fraud involves creating, altering, or using false documents to deceive, while identity theft is the act of stealing and using someone else’s personal information—often to commit fraud.
The two can overlap (e.g., using a stolen identity with forged documents), but document fraud focuses on the authenticity of the document itself, whereas identity theft centers on the misuse of real personal data.
Who needs to know the different types of document fraud?
Understanding the different types of document fraud is critical for teams and decision-makers responsible for preventing financial crime and maintaining trust in digital processes.
Some of those roles include:
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Fraud and compliance teams. To identify and mitigate evolving threats across onboarding and transaction workflows.
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Risk managers. To assess exposure and implement appropriate controls.
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Product managers in fintech and banking. To build secure onboarding flows and KYC processes.
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Identity verification providers. To improve detection accuracy and reduce false positives.
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Law enforcement and regulators. To understand emerging fraud patterns and respond effectively.
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Business leaders and founders. To protect brand reputation, reduce losses, and stay compliant.
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