Document fraud detection with Resistant.AI
Detect document fraud while minimising friction for genuine customers
Detect document fraud and apply the right response within the customer journey
Documents remain important evidence in lending, onboarding, underwriting and claims. But digital documents can be manipulated, generated or reused in ways that are difficult to identify through manual review or conventional document-processing tools.
Resistant AI’s document fraud detection connects document forensics with Experian’s adaptive decisioning capabilities. Resistant AI identifies signs of manipulation, synthetic creation and repeated fraud across PDFs and images. Experian’s adaptive decisioning can then consider those findings alongside relevant customer, credit, identity and fraud information.
170M+ documents analysed globally by Resistant AI
~10% of documents assessed in 2025 were classified as high risk
High-risk documents increased 28.5% year-on-year
Serial fraud increased 7x, with the same documents and techniques reused across submissions
AI-generated document detections increased 90x between January and December 2025
Document detection is only part of the response
Finding evidence of possible document fraud does not, by itself, determine what should happen next.
A forensic result may indicate that a document appears normal, has an unusual origin, shows signs of manipulation or is connected with repeated fraud. The appropriate response will depend on the nature of those findings and the wider context of the application.
An organisation may also need to consider:
- Whether the document supports a lending, onboarding, underwriting or claims decision
- The value or risk associated with the application
- Whether the document information is consistent with other available evidence
- Whether identity, credit or fraud signals indicate additional risk
- Whether alternative documentation can be requested
- The organisation’s policies, referral criteria and risk appetite
A standalone document result may identify a relevant concern, but it does not automatically establish whether the customer journey should continue, another check is required or specialist review is warranted.
This is where decisioning becomes important. It provides the operational framework for translating document evidence into a consistent and proportionate response.
From document intelligence to adaptive decisioning
Resistant AI provides detailed document-forensics findings. Experian decisioning applies those findings within the wider customer and risk strategy.
Connected together, the capabilities can help organisations move beyond a standalone document verdict. Resistant AI findings can become inputs to a decision strategy alongside other relevant information, including application data, identity-verification outcomes, credit information and additional fraud signals.
The strategy can then determine whether to:
- Continue the journey without an additional document-related intervention
- Request another version or an alternative document
- Introduce further identity or fraud verification
- Route the case to an appropriately skilled reviewer
- Stop the process where the organisation’s policy supports that action
The treatment is determined by the organisation’s configured policies, controls and governance requirements. Resistant AI provides evidence about the document, while Experian decisioning determines how that evidence contributes to the operational response.
This creates an adaptive approach in which document controls can reflect the risk identified in each case rather than applying the same treatment to every submission.
How adaptive document fraud decisioning works
The combined workflow connects document assessment with the strategy responsible for determining the next action.
A customer submits a bank statement, payslip, utility bill, invoice, financial statement or other supporting document through an onboarding, lending, underwriting or claims process.
Resistant AI can assess PDFs and image formats through an API or user interface. Its analysis is designed to work across different document types and languages without depending solely on reading or interpreting the document’s written content.
Resistant AI examines characteristics such as document metadata, internal structures, image consistency, fonts and creation artefacts.
A high-risk verdict requires multiple relevant indicators rather than relying on one isolated anomaly. The findings supporting a verdict can be made available for manual review or included in a downloadable report.
The document verdict and relevant forensic attributes enter the decisioning process alongside other information available to the organisation.
Depending on the workflow and configuration, this could include:
- Application and customer information
- Identity-verification results
- Credit information and analytical models
- Device or behavioural intelligence
- Fraud-network or consortium intelligence
- Previous customer or application activity
- Product, policy and eligibility requirements
This broader view can help distinguish an isolated document concern from a case in which several sources of evidence indicate increased risk.
Configured rules, policies, models and workflow determine what should happen next.
A lower-risk case may continue without further document-related intervention. An unusual submission may trigger a request for another document. Conflicting or higher-risk evidence may result in additional verification or specialist review.
The decision strategy can also help direct cases to the appropriate operational path rather than placing every referred document into the same review queue.
Fraud patterns, organisational requirements and risk appetite can change over time.
Authorised teams can review relevant outcomes and update decision logic through controlled testing, approval and release processes. This helps maintain alignment between emerging document risk and the organisation’s operational response.
Analyse documents beyond their visible content
The solution examines technical characteristics including:
- Metadata and document origin
- Internal file structures
- Font usage and consistency
- Image characteristics
- Creation and modification artefacts
- Repeated backgrounds or physical modifications
- Relationships with previously assessed documents
Resistant AI uses more than 500 detection models trained on global data to analyse relevant document characteristics. A high-risk verdict requires multiple indicators rather than being triggered by one finding alone.
This is different from extracting information from a document. A document-processing system may be able to identify a value or populate an application field without establishing whether the underlying document has been manipulated.
Document forensics adds another question to the process: can the submitted evidence itself be trusted?
Identify serial and organised document fraud
Serial fraud occurs when the same document, template, production method or manipulation technique is used across multiple submissions.
Individual documents may appear unrelated when reviewed in isolation. Resistant AI can compare relevant document and image characteristics to identify patterns such as:
- Reused source documents
- Repeated backgrounds
- Related production methods
- Template-based alterations
- Similar manipulation techniques
- Coordinated multi-document activity
Resistant AI reports that 98.3% of the serial fraud it detects is identified without prior knowledge of the underlying template. This indicates that repeated attack characteristics may remain detectable even when the source template has not already been catalogued.
Within an adaptive decision strategy, evidence of reuse or coordinated activity can receive a different treatment from an isolated document anomaly.
Supporting document-heavy customer journeys
Adaptive decisioning with Resistant AI can be applied wherever customer-supplied documents influence a risk, onboarding or commercial decision.
Lenders may collect bank statements, payslips, financial statements, invoices, asset records and other documents to support an application.
These documents may contain information that credit bureau data alone cannot establish, such as income, assets, collateral, source of wealth or the financial position of a business.
Resistant AI can identify signs of digital manipulation, synthetic creation or repeated document fraud. Experian decisioning can then consider those findings alongside credit policy, application information, identity evidence and other fraud signals.
Relevant use cases may include:
Home lending
Motor and asset finance
Commercial and business lending
Invoice and receivables finance
Trade finance
Commercial property lending
This may be particularly relevant to higher-value or document-heavy applications where one fraudulent submission can result in a significant loss and manual authentication can delay the credit decision.
KYC and KYB processes may involve proof of address, source-of-funds information, ownership records, registration documents, bank statements and other third-party evidence.
Document-forensics findings can contribute to a wider onboarding strategy that also considers identity verification, watchlist screening and relevant fraud intelligence.
This can help distinguish between submissions that may continue, cases requiring another form of evidence and higher-risk applications requiring specialist attention.
Claims and underwriting processes often depend on invoices, reports, statements and other digital evidence.
Resistant AI can assess these documents for signs of manipulation or repeated use. Decisioning can then determine whether the process should continue, another document should be requested or the case should be referred for investigation.
This can support faster handling of lower-risk cases while maintaining additional controls where the document evidence warrants them.
Business onboarding can involve varied document types, issuers, formats and languages, particularly where organisations operate across several markets.
Connecting document forensics with decisioning can help distinguish routine submissions from documents requiring clarification, further verification or specialist review.
170 million
More than 170 million documents analysed globally
500
More than 500 detection models
20 seconds
Document assessment in under 20 seconds
Three times
Up to three times more fraud detected
10 times
Up to 10 times fewer manual reviews
SOC 2 Type 2 controls
Reported performance and commercial outcomes are based on Resistant AI data and client implementations. Results will vary according to document mix, fraud exposure, configuration, existing controls and operating environment.
Identity verification
Document and biometric verification
Device and behavioural intelligence
Document forensics
Fraud consortium and network intelligence
Global watchlist screening
Credit data and analytical models
Decision orchestration and workflow
Explore the ecosystem
Relevant services can be introduced according to the specific challenge and customer risk profile rather than applying every check to every applicant. This supports a layered approach in which different signals contribute to the overall assessment.
Resistant AI can operate within Experian’s connected fraud and decisioning ecosystem. Ascend Platform brings together data, analytics, identity, fraud and decisioning capabilities within a shared operating environment, while Experian PowerCurve solutions support the strategies and workflows through which operational decisions are made.
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Frequently asked questions
Resistant AI is a document-forensics solution that analyses PDFs and images for indicators of manipulation, synthetic creation, reuse and serial fraud.
It provides verdicts and supporting findings that can be reviewed directly or used within a decision workflow.
Adaptive decisioning uses available data, fraud signals, analytical outputs, policies, rules and workflow to determine an appropriate action for each case.
Decision strategies can be refined as fraud patterns, customer behaviour, business requirements and policies change.
Resistant AI’s verdicts and relevant forensic attributes can be used as inputs to supported Experian decisioning workflows.
The decision strategy can consider these findings alongside other customer, identity, credit and fraud information before continuing the journey, requesting more evidence, introducing another check or referring the case for review.
- Adaptive decisioning with Resistant AI may be relevant where an organisation:
- Relies on customer-supplied documents during onboarding, lending, underwriting or claims
- Finds sophisticated document alteration difficult to identify manually
- Receives documents from many issuers, countries or in varied formats
- Has high volumes of document-related referrals
- Needs to distinguish suspicious manipulation from lower-risk technical changes
- Wants clearer evidence to support document-fraud investigations
- Wants document findings to inform approval, referral and further-verification workflows
- Needs to adapt its response as fraud patterns and risk appetite change
- Is seeking to connect document forensics with existing identity, credit or fraud controls
The appropriate solution design will depend on the use case, document volumes, existing controls, technology environment, risk appetite and applicable obligations.
No. Resistant AI provides document-forensics findings. How an organisation responds is determined by its configured policies, decision strategy, other available evidence and governance requirements.
Resistant AI can assess PDFs and images across a broad range of consumer, financial and business documents.
Examples include bank statements, payslips, utility bills, invoices, financial statements, business-registration documents and proof-of-address records. Suitability for a specific document format or use case should be confirmed during solution design.
No. Identity-document verification generally assesses a government-issued identity document and may compare it with a biometric capture.
Resistant AI provides document forensics across a broader range of digital evidence, including financial and business documents.
The two capabilities can be used together where a customer journey requires both identity proofing and assessment of supporting documents.
No. A normal verdict means that no evidence of document manipulation has been identified.
It does not independently confirm that the information is accurate, that the issuer is legitimate or that the document belongs to the applicant. Other relevant verification and decision controls may still be required.
Resistant AI is designed to identify indicators associated with synthetic and AI-generated documents alongside other forms of manipulation.
Detection methods and fraud patterns continue to evolve, so document forensics should form part of a layered fraud strategy rather than being treated as a control capable of identifying every fraudulent submission.
Resistant AI includes capabilities designed to identify serial fraud and repeated document characteristics across submissions.
The available comparison, implementation and privacy arrangements will depend on the solution configuration and permitted use.
Yes. Relevant document verdicts and forensic attributes can contribute to configured decision policies and workflows.
The organisation determines how different findings are treated, subject to the implementation design, available capabilities and applicable governance requirements.
Not necessarily. Resistant AI is designed to support automation of suitable cases and direct specialist attention towards relevant exceptions. Manual review may remain appropriate where information is incomplete, findings conflict or judgement is required.
Resistant AI reports document assessment in under 20 seconds. Actual response times may vary according to the document, workflow, integration and service conditions.