In 2026, document intake volumes are reaching record highs. It’s no longer optional to automate. So much so that the global intelligent document processing (IDP) market is expected to grow from USD 14.16 billion in 2026 to USD 91.02 billion by 2034.
When done right, intelligent document processing can increase an institution's ROI, decrease human errors, and accelerate workflows.
But in high-risk sectors, it can expose your systems to document fraud.
In an era where institutions feel the growing pressure to automate everything, fraudsters are exposing those hastily built systems and driving waves of automated fraud through the cracks. It’s truly a battle of technology vs. technology, with AI capabilities controlling the front lines.
That’s why the best way to automate your intelligent document processing is with AI document verification.
Here’s why:
What is document processing, and how do you automate it?
Document processing is the transformation of data contained in documents - physical and analog - into digital data that can be fed into business systems and processes or stored for future references.
AI-based document processing utilizes the following methods:
- Optical character recognition (OCR): The digitization of scanned, photographed or PDF documents that converts handwritten or printed texts into machine readable data.
- Intelligent document processing (IDP): Incorporates the use of OCR to capture, extract, and process data in business documents for any industry–no matter the type of departmental process. It works by using various AI models such as machine learning, generative AI, natural language processing (NLP), to understand the meaning of the document and information it contains in a human-like manner.
What are the risks of automated document processing?
With any technology, certain risks are posed. Here are some of the most common risks associated with automated document processing:
Document fraud
No matter how effective automated document processing is, it is still prone to document fraud. While document validation (checking whether the data within a document conforms to a predefined set of rules, formats, or expected values) significantly increases touchless processing rates, neither document verification (confirms a document's authenticity, integrity, and origin) nor document authentication (confirming that a document comes from the authority, issuer, or source it claims to come from) have been performed as part of this step.
Meanwhile, 1 in 3 documents show signs of tampering, 1 in 10 of those can be classified as high risk. Add on the 1 in 50 of those high-risk documents that show signs of serial fraud, and you have a massive problem that needs to be addressed.
Automated business processes, especially customer-facing ones, need IDP solutions, that work in lockstep with fraud detection to keep up with this threat in 2026.
Unreliable large language models (LLMs)
LLMs came into the picture with a slew of other issues. From data privacy concerns to data poisoning, LLM reliance on initial training through large datasets of texts and code makes them prone to biased decisions and has difficulties processing complex layouts, typical for business documents like we mentioned above.
In addition, LLMs are known to “hallucinate” in cases where they don’t know, have the wrong answer, or sometimes just as a shortcut to doing the actual work, providing inaccurate information which can be detrimental to businesses.
Lastly, fraudsters have been known to use LLMs to create fake documents such as receipts, utility bills, flight tickets, and more.
How to fight fraud in document automation?
With template farms garnering over 1.7M monthly visits, and LLMs garnering billions of weekly active users, modifying or creating fraudulent documents has a low barrier to entry.
Depending on the company, defending against these crimes is usually a multi-faceted approach that aims to strengthen fraud security.
As the saying goes, “prevention is better than a cure.”
For most businesses, fraud prevention usually involves identifying fraudulent documents through an in-house fraud detection specialist or risk team, manually reviewing cases one by one.
Other institutions will build rules and thresholds, pre-defining what fraud looks like and flagging submissions that meet that criteria.
Both approaches are slow and retroactive.
The best fraud detection software goes a step further, using AI to analyze how documents are constructed, connect the dots between submissions, and spot similarities as warning signals to improve the system over time.
Using AI to read documents is just the beginning for document processing services–the next step should be document forgery detection. And it's always better to do it with AI than manually.
Here’s why:
Manual screening
For organizations that already automate document processing, using manual reviews defeats the purpose of efficiency. Every document has to be verified by a human, putting manual workload back into what is supposed to be a mostly automated process.
Therefore, firms should consider incorporating document intelligence with fraud prevention services such as a fake document checker, which helps to catch sophisticated fraud. Otherwise, case escalations can plague your review teams and create bottlenecks for your business. It will be harder to expand into new markets and will create unnecessary friction for potential customers.
AI document fraud detection in document processing
AI detects 30% more fraud than manual reviews, making it an incredibly useful tool for fraud detection specialists, business owners, underwriters, and even analysts.
Instead of letting people guess if a document is fraudulent, AI detection goes one step further to show you exactly how a document triggered an alert and give you confident evidence for any decision your team makes.
To be effective at scale, document fraud detection should be part of a broader risk strategy. This typically includes:
- Risk-based decisioning: Prioritize high-risk cases and reduce manual workload.
- Seamless KYC onboarding: Verify identity and documents without adding friction.
- Cross-document analysis: Detects inconsistencies across multiple submissions, not just within a single file.
- Continuous learning systems: Adapt to new fraud patterns as they emerge.
- Auditability and explainability: Provide clear reasoning behind every decision for compliance and review.
Conclusion
All of the benefits presented by AI document fraud detection capabilities above are available on the Resistant Documents platform. It can detect up to 3X more fraud, reducing manual reviews by 90% while quintupling your review speed.
For safety and security to be the cornerstone of any document processing procedure, the best approach is to address document fraud at the same time as document processing.
About the ABBYY and Resistant AI partnership
ABBYY and Resistant AI have partnered to help organizations strengthen document-driven risk and fraud prevention in increasingly complex threat and compliance environments. Together, ABBYY’s intelligent document processing (IDP) capabilities and Resistant AI’s fraud detection technology provide a complementary approach that enables businesses to both extract and process document data efficiently and verify document authenticity and integrity. Recognizing the growing need for trusted, AI-driven document workflows in highly regulated industries, the companies have established a business partnership and offer an integration connector through the ABBYY Marketplace to simplify deployment and interoperability between both platforms.