Document processing
10 min read
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Oct 28, 2025
Smarter claims, shorter waits, happier customers. Discover how AI claims automation can accelerate and enhance insurance claim workflows.
Imogen Jones
Content Writer
Meet Harry, an experienced claims processor working at a small firm. He's just finished retyping the same policy number for the third time today. afternoon, he’ll be knee-deep in a crowded inbox, copying details from one PDF after another. Somewhere in between, he'll chase an adjuster for an update and explain to a customer why their claim is still under review.
Like many claims processors, far too much of his time is eaten up by manual data entry, copy-paste, and endless system hopping that adds little real value. Accenture found up to 40% of claims underwriters’ time is spent on non-core and administrative activities, representing an industry-wide efficiency loss of up to $160 billion over the next five years.
Highly manual processes come with other drawbacks, too. The financial toll compounds with every error, correction, and delayed settlement, ultimately impacting customer satisfaction and retention.
Modern artificial intelligence, especially agentic AI, is changing that dynamic. These systems can understand context, distinguish intent, and can automate entire workflows, from intake and validation to fraud detection and settlement.
In this article, we’ll explore:
The evolution of claims processing from manual workflows to AI agents
How AI agents automate and improve the claims lifecycle
Key considerations for successful implementation and deployment
The Evolution of Claims Automation
For decades, insurance claims processing has been one of the most resource-intensive and time-consuming operations in the industry. Every claim represents a flood of information that must be collected, verified, and interpreted, a process that relies as much on human effort as on technology.
Manual and Early Digital Claims Processing
In the early days, claims management was an entirely manual enterprise. Physical documentation filled filing cabinets, and adjusters relied on phone calls, forms, and memory to keep cases moving.
The first real leap came in the 1990s, when insurers began implementing digital records and document management systems. Databases replaced filing cabinets, making storage and retrieval faster and more reliable. The arrival of Optical Character Recognition (OCR) added another layer of efficiency, allowing text from scanned pages to be captured and indexed.
The shift from paper folders to digital claims systems was a huge step forward, but not the finish line.
The Rise of Claims Automation and AI
The 2010s introduced Robotic Process Automation (RPA) and early machine learning models. These tools could copy data between systems or flag anomalies, delivering measurable gains in efficiency. Insurers also began to experiment with automated fraud detection and predictive analytics.
But progress soon plateaued. Traditional automation depends on structured inputs, while insurance data is anything but. Claims still arrive as messy collections of PDFs, emails, images, and handwritten forms. Adjuster notes, medical reports, and correspondence contain valuable insight, but they’re locked away in unstructured formats.
Meanwhile, legacy systems piled up.
The current web of solutions and legacy systems also leads to increased system complexity which, in turn, often results in increased costs, manual intervention and opportunities for errors. It can also create poor visibility across the end-to-end claims process. From claims notification through to closing and payment, dozens of third parties and suppliers may be involved in the process — each with their own systems and data.
— KPMG
Recent advances in generative AI and Large Language Models mark the next major shift. These systems can read, interpret, and act on unstructured data (from a scanned medical report to an adjuster’s free-text note) with human-like understanding. For insurers, this opens the door to more holistic automation, where systems can reason across documents, not just extract data from them.
There are significant early signs that AI for insurance is improving outcomes across the board, with McKinsey reporting that companies who embrace a domain-based approach to AI implementation are already seeing "a 10 to 20 percent improvement in new-agent success rates and sales conversion rates, a 10 to 15 percent increase in premium growth, a 20 to 40 percent reduction in costs to onboard new customers, and a 3 to 5 percent accuracy improvement in claims."
AI Agents for Claims Automation
Among the various approaches to AI in claims, AI agents have emerged as a particularly exciting development. An AI agent is an autonomous system that combines reasoning, workflow execution, and data connectivity. It can make contextual decisions, interact with multiple systems, and adapt its actions to meet defined goals, such as validating policy data, triaging claims, or detecting fraud, with minimal human intervention.
In insurance, this means an agent can intelligently automate significant portions of the claims lifecycle, from intake to resolution.
See below V7 Go's Insurance Claims Automation Agent, which can read entire submission packets (including claim forms, police reports, photos, and invoices) then extract, validate, structure and analyze all the data needed to set up a new claim.
You can learn more about agents in our blog, What Are AI Agents and How to Use Them in 2025?
AI Agents for Claims Automation: Example Workflow
AI agents transform claims processing by orchestrating a series of intelligent actions. Instead of a linear, manual hand-off, an agentic workflow is a dynamic, multi-step process.
Insurers excelling in AI rely on a flexible AI capabilities stack powered by reusable multiagent systems. The modern AI tech stack for an insurer is highly modular and flexible to cope with fast-changing technology.
A typical AI agent workflow for claims processing might look like the following:
Intake & Classification: An AI agent ingests a new claim submission, which could be an email with multiple attachments in various formats (PDFs, images, etc.). It automatically classifies each document type, be it a First Notice of Loss (FNOL), medical bill, police report, or repair estimate.
Data Extraction: The agent applies the appropriate AI model to each document, extracting relevant data. It uses advanced OCR to read scanned text and LLMs to understand the context of narratives in adjuster notes or medical summaries.
This dual-layered approach allows the system to capture both facts and context, producing structured, high-quality data ready for analysis or validation.Validation & Enrichment: The agent cross-references the extracted information with the policyholder's data.
It can also enrich the dataset with external sources, such as vehicle history databases, weather reports, or provider registries. This enrichment step ensures each claim file is accurate, complete, and fully contextualised before moving forward.Fraud Detection & Analysis: With a complete and validated data set, the agent runs predictive analytics models to assess fraud risk. These models look for inconsistencies, duplicate claims, or patterns that align with known fraud indicators. For example:
Repeated claims for similar incidents across regions
Inconsistent timestamps between documents
Suspicious billing from medical providers or repair shops
When anomalies are detected, the agent flags the case for human review or escalates it to a specialist AI agent focused solely on fraud detection.
Decision & Routing: Finally, the agent determines how to resolve the claim.
Low-risk, high-confidence cases can be automatically adjudicated.
Complex or borderline cases can be flagged for human-in-the-loop review.
With the AI agent accelerating time-consuming manual processes, cycle times shrink dramatically, accuracy improves, and adjusters are free to focus on exceptions and customer communication rather than rekeying data.
V7 Go: AI for Insurance Underwriting & Claims Automation
V7 Go is an AI platform designed to automate complex, document-intensive workflows, including multimodal document processing, fraud detection, and complex claims handling.
Insurers can design and deploy custom AI agents that work in line with their unique business rules, risk models, and compliance frameworks. Whether it’s accelerating First Notice of Loss triage, streamlining policy reviews, or automating loss adjustment, the platform brings greater speed and accuracy to every stage of the claims lifecycle.
Key Features for AI Claims Automation:
Go leverages frontier multimodal AI models to interpret even the most complex or lengthy insurance documents, including medical reports, legal correspondence, adjuster notes, and claim summaries. It extracts and connects information across formats, eliminating manual validation bottlenecks and accelerating claim resolution.
Claims processors can easily run robust analysis of unstructured and structured data, with numeric calculations and Python support for complex risk or pricing operations.
V7 Go was designed for regulated industries. All customer data is processed within isolated, secure environments, with granular access controls and full encryption in transit and at rest. This ensures sensitive customer and claims information remains private and protected at every stage of automation.
AI agents can identify and maintain relationships between data points within complex tables, graphs, and embedded visuals (such as those in loss runs, medical invoices, or actuarial reports) preserving structured data integrity.
As shown below, you can interact with the system in plain natural language through the Concierge, making powerful AI processing and insight accessible to teams across operations, claims, and compliance.
Just as importantly, every extraction, prediction or answer supplied by V7 Go’s AI agents is traceable. The AI Citations feature links each extracted field back to its exact location within the source document, down to the sentence or line. This audit trail allows insurers to verify how a decision was derived, a critical requirement for compliance, regulatory audits, and model governance.
By automating repetitive manual work while maintaining strict auditability and governance, V7 Go enables insurers to accelerate claims turnaround times, reduce operational costs, and focus human expertise where it truly matters: complex risk evaluation and customer experience.
Looking for a complete guide to software for automated and AI claims processing? Read our guide, Best Automated Claims Processing Software [2025].
AI Agents for Claims Automation: A Case Study
Trent-Services is a third-party administrator providing claims handling and policy management for insurers across the UK. Their portfolio includes their own line of products under Corinium Insurance Services, which offers pet, income protection and mortgage protection insurance.
Before V7 Go Trent-Services’ assessors spent much of their time on the administrative tasks typical of claims processing; rekeying data, reviewing documents, and navigating multiple systems.
"With manual administration comes human error. You’re churning out 10 to 15 claims a day. So much of your time is being taken up by typing, and things can easily be missed when you have to go between different screens," explained Lewis, Claims Manager at Trent-Services. "Your reputation is at risk if insurers come to you saying, ‘you have errors here and there’, and customers are frustrated if SLAs slip."
Using V7 Go, the team built an intelligent claims assessment agent capable of:
Extracting and standardizing data from pet insurance invoices.
Flagging potential pre-existing conditions based on claims data and medical history.
Performing downstream calculations on treatment invoices for accuracy and completeness.
“We have six assessors. Before V7 Go, each would process around 15 claims a day, about 90 in total. With V7 Go, we’re expecting that to rise to around 20 claims per assessor, which adds up to an extra 30 claims a day. That’s the equivalent of two additional full-time assessors. Beyond the cost savings, there’s real reputational gains from fewer errors and faster turnaround times,” says Lewis.
To learn more about Trent-Services journey with V7 Go, check out their full case study here.
Key Considerations for AI Claims Automation Implementation
Successfully implementing automated claims processing requires more than just choosing the right software. Insurers must focus on seamless integration with existing core systems like policy administration and CRM platforms. Effective integration is critical to avoid data silos and ensure a single source of truth across the claims lifecycle.
Equally important is process alignment. Claims workflows must be clearly defined and standardised before automation begins, because automating a broken or inconsistent process only accelerates inefficiency. Leading insurers start by mapping the claims journey (from FNOL through adjudication to settlement) and identifying the manual decision points that AI and automation can augment.
Change management also plays a critical role. Training teams to understand and trust AI outputs, establishing clear escalation rules for exceptions, and maintaining regulatory compliance are all part of a successful rollout.
For a complete and detailed guide to claims automation implementation, check out our Automated Claims Processing: Implementation Guide for Insurance Operations.
If you want to learn more about AI solutions for claims automation, book a demo with our team and tell us more about your specific needs and the processes you'd like to improve.
