The biggest threat to your onboarding process may not be a stolen identity document, but rather one that never existed in the first place.
AI-powered image generation and editing tools have made it easier than ever to create convincing synthetic and manipulated identity documents. Fraudsters can alter legitimate IDs or generate fake credentials to bypass traditional verification checks with increasing sophistication.
As these attacks become more common, you need an identity verification solution that can identify AI-generated and digitally altered documents in real time – before fraudulent applicants are approved and onboarded.
We are an example of a provider that uses advanced forensic analysis to spot these high-tech forgeries.
AI-generated and digitally manipulated identity documents can be difficult to spot using traditional verification methods alone. Effectively defending against these threats requires a layered approach that can assess document authenticity, confirm identity ownership and uncover signs of fraud throughout the onboarding journey.
We help organizations detect these fraudulent identity documents by combining document authenticity analysis, biometric verification and advanced fraud intelligence within a single onboarding workflow.
Our KYC orchestration platform, GBG Go, supports government-issued identity documents from 195 countries and helps businesses verify genuine customers while identifying emerging fraud threats in real time.
You might start your onboarding process with a simple visual check, only to realize later that a fraudster used a high-resolution pixel editor to change a date of birth or swap a photo.
Standard verification often misses these digital artifacts because the human eye can't see the microscopic inconsistencies left behind by generative artificial intelligence.
We close this gap by moving beyond the surface and performing over 50 automated forensic tests on every document in seconds.
Our system interrogates the document for photo substitutions and forged security features like holograms, microprinting and ultraviolet patterns that are nearly impossible to replicate perfectly in a digital environment.
But confirming that a document is genuine is only part of the challenge. Fraudsters may still attempt to use authentic documents that belong to someone else. To prevent these attacks, you must also verify that the person presenting the document is its rightful owner.
This is where our biometric verification technology can help: it uses 68 facial landmarks to compare an applicant's live selfie against the photo on their verified identity document. By using biometric facial recognition, you ensure that the person behind the screen is actually the person on the credential, not a deepfake or a high-quality mask.
Our passive liveness detection serves as another critical barrier against spoofing attacks that use pre-recorded videos or static photos to bypass automated gates.
"When it comes to biometric matching, the algorithms we deploy are much better than human beings. A human reviewer looking at a small black-and-white passport photo can't reliably tell two similar-looking men apart. Human beings are far more easily fooled than the facial matching algorithms we use – and that matters because every document includes a portrait photo," says David Thomas, Global Head of Product (Documents and Biometrics) at GBG.
Verifying that a document is genuine and belongs to the applicant is a critical first step. But sophisticated fraudsters often leave clues that extend beyond the identity document itself.
Patterns such as repeated signup attempts, unusual device activity, or inconsistencies across customer data can reveal coordinated fraud that document and biometric checks alone may not catch.
By analyzing additional risk signals throughout the onboarding journey, you can:
Incorporating velocity checks into biometric solutions also allows organizations to see if a specific face has appeared across multiple different accounts or documents, which is a telltale sign of organized fraud.
As your business expands into new markets, identity verification becomes more complex. A document that's instantly recognizable in one country may be unfamiliar in another, creating unnecessary friction for genuine customers and increasing the likelihood of manual reviews.
We help remove these barriers through a continuously updated document library that supports more than 8,500 identity document types across 195 countries. This broad coverage enables you to verify customers confidently across markets while maintaining a consistent onboarding experience.
Our platform then transforms these verification checks into real-time decisions through automated KYC orchestration. You can configure workflows that automatically approve low-risk applicants, route higher-risk cases for additional verification and apply enhanced due diligence only when required.
By automating identity verification and risk-based decisioning, your team can reduce manual review volumes, accelerate onboarding and focus investigative resources on the cases that genuinely require attention.
A major sports betting operator faced a surge in bonus abuse as fraudsters used digitally altered documents to create multiple accounts and drain promotional funds.
We helped them implement a layered verification workflow that uses adaptive customer onboarding to instantly approve low-risk players while routing suspicious applicants to biometric checks.
This strategy delivered a 13:1 return on investment and increased auto approval rates by 80% for difficult-to-verify demographics.
Forging a passport used to require specialized hardware and high-end printing materials. Today, a fraudster only needs a laptop. Generative AI has lowered the barrier to entry, allowing bad actors to produce hyper-realistic fake documents at a scale that was previously impossible.
Fraudsters typically use AI image generation tools to create synthetic faces or entirely new ID templates. They also use deepfake technology to swap faces on legitimate documents they’ve found on the dark web.
Common methods for altering IDs include pixel-level editing to change birth dates or expiration dates, making an old or stolen ID appear valid for a new account application.
The business impact of these tactics is severe:
Traditional document checks often rely on manual oversight, but human reviewers can easily be fooled by the high resolution and precision of AI-generated documents.
Without automated fraud detection that can spot pixel-level anomalies and metadata inconsistencies, your onboarding process remains vulnerable.
To keep up with modern threats, identity verification must evolve beyond simple image capture. You need a tech stack that can interrogate every layer of a digital submission.
Advanced document authenticity analysis is the first line of defense. The system must look for signs of digital manipulation by identifying inconsistencies in fonts, layouts and security features. If a fraudster uses AI to generate an ID, there are often subtle image artifacts or generation patterns that machine learning models can detect even when the human eye can't.
A robust solution should include:
For example, a digital bank might use an automated workflow that first checks if a submitted email address is linked to known fraud via a solution like our GBG Trust. If the email appears clean, the user scans their ID. The system then uses AI to check for tampering while simultaneously matching the user's face to the ID photo. This entire process happens in seconds, keeping friction low for the customer.
Choosing the wrong vendor can leave your business exposed to high risk or lead to frustrated customers quitting your signup process. When evaluating providers, avoid the following:
Document analysis is important, but it rarely provides a complete picture of risk: a convincing AI-generated identity document may appear authentic on its own.
The strongest onboarding programs combine document verification with biometric face matching, liveness detection, device intelligence, behavioral signals and risk assessment to identify fraud that a single check could miss.
Stopping fraud matters, but so does onboarding genuine customers. Some solutions reduce risk by creating excessive friction or flagging large numbers of genuine applicants for manual review. Look beyond a provider's fraud detection claims and assess their impact on:
Fraud tactics evolve quickly, and identity documents change regularly. A provider may support thousands of document types today, but that coverage becomes less valuable if its document library isn't continuously updated.
Ask how frequently new document templates, security features, and emerging fraud patterns are added to a provider's platform. This is particularly important if you're onboarding customers across multiple countries and jurisdictions.
Many vendors now advertise AI-generated document detection, but not all approaches are equally effective. Instead of focusing solely on whether a provider claims to detect AI-generated content, evaluate how that capability fits into the broader verification workflow.
Ask what fraud signals are analyzed, how risk decisions are generated, and if the platform can respond when fraud patterns change. Providers that combine document analysis with adaptive risk intelligence are often better positioned to keep pace with rapidly evolving fraud techniques.
A solution may perform well during testing but struggle under real-world onboarding volumes.
Consider factors such as real-time decision speeds, automation capabilities and workflow flexibility. The right solution should help your fraud team scale efficiently as onboarding volumes grow.
AI-generated documents are a sophisticated threat, but they don't have to be a blocker to your growth.
By using automated authenticity analysis, biometric matching and real-time risk orchestration through GBG Go, you can stop fraudsters early on in the onboarding process.
Modern solutions use computer vision and machine learning to analyze pixel-level artifacts, metadata inconsistencies and security feature anomalies that are common in AI-generated media. They also use liveness detection to ensure a real person is submitting the document.
AI-generated IDs are now so precise that they can mimic fonts, holograms and layouts with near-perfect accuracy. Human reviewers can't spot the pixel-level discrepancies or metadata red flags that automated systems identify instantly.
Yes, provided the vendor maintains a global document library. Systems like those from GBG support more than 8,500 document types from 195 countries, ensuring that regional security features and naming conventions are accounted for during the verification process.
An injection attack involves bypassing a device’s camera to feed pre-recorded or AI-generated footage directly into the verification system. A deepfake is the actual AI-generated media used to alter a person’s face or voice. A robust security stack must detect both.