Document processing
12 min read
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Feb 4, 2025
Content Creator
The insurance industry has a secret that vendors don't talk about: most standard underwriting is already automated. Fannie Mae's Desktop Underwriter (DU) and Freddie Mac's Loan Prospector (LP) systems have been handling straightforward cases for quite a while. The real challenge? Everything that doesn't fit neatly into these systems.
Consider a commercial property with unique environmental risks, or a specialty insurance policy for an emerging technology company. These cases don't slot into standard document templates or rule-based workflows. Instead, underwriters spend hours manually processing complex documentation, analyzing non-standard risks, and piecing together insights from disparate sources.
This is where generative AI makes a real difference—not by reinventing standard automation, but by tackling the unstructured, non-standard cases that existing systems can't handle. According to a recent report by Deloitte, 76% of surveyed insurance executives indicated that their organizations have implemented generative AI capabilities in one or more business functions. AI is a game-changer for both efficiency and accuracy.
In this article, we'll explore:
Why standard automation isn't enough for today's insurance landscape
How AI bridges the gap for non-standard cases
Real capabilities of modern AI systems vs. traditional automation
Leading platforms and their practical applications
Implementation strategies that work
The future of intelligent automation in insurance
Bridging the Automation Gap in Insurance Underwriting
There's a common misconception in the insurance industry about AI's role in underwriting. Many underwriters see all AI underwriting tools as Optical Character Recognition (OCR) tech in disguise.
This skepticism isn't entirely misplaced. Many basic operations are already digital, and major financial institutions use sophisticated automated underwriting systems with AI-like capabilities embedded in their core infrastructure. LoanDepot's MelloNow technology, for instance, processes loans from origination to conditional approval automatically. These aren't simple rule-based systems—they perform complex risk calculations and automated decisioning that many AI vendors are pitching as innovative features.
However, this perspective misses a crucial distinction: generative AI systems, particularly those using Large Language Models (LLMs), go far beyond simple document scanning or digital data transfer. Consider Zurich Insurance's implementation of Azure OpenAI Service. Their system doesn't just digitize documents—it comprehends complex unstructured information across multiple languages and formats. As Christian Westermann, Zurich's Group Head of AI explains, "We provide insurance to our customers in locations all over the world, and they often give us a lot of different data: reports, images, emails—all in different languages and formats. By embracing tools like Azure AI, we're able to make better sense of a complex world."
The real value of modern AI emerges in its ability to understand and process unstructured information that traditional automation can't handle. While basic automation excels at moving structured data between systems, AI can analyze natural language in risk reports, interpret complex policy language, make connections between seemingly unrelated data points, and process non-standardized formats from emails to technical specifications.
This capability represents a fundamental shift in what's possible. Traditional automation handles structured data well, but it can't tackle the complexity of unstructured information that makes up much of an underwriter's daily workflow when dealing with non-standard cases. Modern AI fills this gap by making sense of complex information at scale, enabling underwriters to focus on more than just manual document processing.
AI document Ingestion in insurance: Document ingestion represents a critical first step in the underwriting process. It encompasses capturing, classifying, and processing various documents—from claims forms and policy applications to complex underwriting files—into digital formats for efficient handling. Traditionally, this process has been notoriously labor-intensive, prone to errors and delays that impact both operational efficiency and customer satisfaction.
The elimination of manual data entry not only reduces errors but also creates more reliable datasets for decision-making. Perhaps most importantly, faster processing of claims and applications directly improves customer satisfaction and retention.
It is important to identify which scenario and use case is most relevant to your firm. There is no one-size-fits-all solution, as different AI tools offer unique functionalities and frameworks for solving problems.
So, how do you decide? Explore the comparison below for a comprehensive overview of the available options.
Best AI Insurance Underwriting Platforms
Modern AI underwriting platforms broadly fall into two categories: specialized solutions designed specifically for complex underwriting scenarios and AI platforms and enterprise solutions with capabilities that can be easily adapted to insurance use cases.
Note that this is not a ranking of competing solutions with identical functionalities. Instead, the platforms listed below tackle various underwriting challenges in distinct ways, utilizing different frameworks and approaches. Read the overview to learn more about what the key players in the market offer.
Now, let’s discuss each of these solutions in more detail.
V7 Go is an AI platform for automating complex tasks across various data types, including documents, images, text, and audio. The platform leverages generative AI models to streamline processes in industries such as finance, legal, logistics, insurance, and healthcare. The platform has quickly gained traction, with over 1,000 team sign-ups and recognition as one of Google's top 100 GenAI partners.
Key Functionalities:
Document Automation: Transforms complex documents into AI workflows, enabling efficient data extraction and processing
AI Insurance Claims Processing: Automates the handling of insurance claims, reducing manual effort and improving accuracy
Property Insurance Analysis: Analyzes property insurance documents to extract and assess critical risk factors
Policy Application Processing: Streamlines the processing of policy applications, accelerating approvals with advanced document workflows
AI Risk Engineering: Evaluates technical diagrams and specifications to extract crucial information for risk assessments
The platform offers pre-built AI agents and workflows for underwriting use cases, which can be customized without coding, allowing teams to be operational within approximately seven days. V7 Go provides seamless integration with existing tech stacks through its open API, Zapier, and native integrations via cloud providers.
Pros:
High Volume & Batch Processing: Capable of handling hundreds of submissions simultaneously, V7 Go automatically classifies insurance documents, extracts key data, and validates information, accommodating various formats such as PDFs, scans, and images.
Advanced Security and Compliance: Adheres to highest standards for data protection and ensures sensitive insurance data is safeguarded with enterprise-grade security features and compliance (SOC 2 Type 2, ISO 27001, GDPR) with international insurance regulations.
Customizable User Permissions: Offers customizable user roles and project-level permissions, allowing for controlled access across underwriting teams and management of complex insurance operations.
If you are ready to streamline your insurance operations with AI, book a call with V7. Share examples of your underwriting or claims data to discover practical solutions tailored to your specific challenges.
Multimodal is an AI platform designed to automate complex workflows in the insurance industry, with a particular focus on underwriting processes. By deploying AI agents trained on company-specific data, Multimodal streamlines tasks such as document processing, data analysis, and decision-making, thereby enhancing efficiency and accuracy in risk assessment.
Key Functionalities:
Insurance Submission Intake & Triage: Automatically processes, prioritizes, and routes submissions to appropriate underwriters based on predefined guidelines
Risk Assessment: Utilizes intelligent algorithms to identify risk factors, assess claim likelihood, and detect potential fraud by analyzing applicant information and market conditions
Data-Driven Decision Support: Generates underwriting reports and drafts policy documents, providing actionable insights to underwriters for informed decision-making
The platform's AI agents are pre-trained on public data and fine-tuned with proprietary company data, ensuring a customized fit for specific workflows. Integration with existing systems is facilitated through a chat interface and database connections. Implementation can be achieved in as little as 8-9 weeks.
Sixfold is a generative AI platform specifically designed to enhance the efficiency, accuracy, and transparency of insurance underwriting processes. By automating manual tasks, Sixfold enables underwriters to focus on high-value decision-making, thereby improving overall productivity.
Key Functionalities:
Triage Submissions: Highlights submissions that best align with an insurer's risk appetite, streamlining the initial review process
Detect Risk Signals: Identifies positive, negative, or disqualifying risk factors within applications, providing underwriters with critical insights
Identify Inconsistencies: Spots discrepancies across application documents and data sources, ensuring accurate and reliable assessments
Summarize Data: Synthesizes information into concise, plain-language summaries, facilitating quicker decision-making
AI Adoption in Insurance: Almost 70% of sales and underwriting teams are currently piloting large language models in their workflows. However, while initial enthusiasm is high, many companies are discovering the practical limitations of LLMs when it comes to handling complex insurance tasks, as they transition from pilot programs to real-world implementation. Check
Azure AI is a comprehensive suite of artificial intelligence services designed to enhance various industries, including insurance underwriting. By integrating Azure AI into underwriting processes, insurance companies can achieve greater efficiency, accuracy, and customer satisfaction.
Key Functionalities:
Advanced Data Analysis: Azure AI enables underwriters to analyze vast amounts of data swiftly, identifying patterns and risk factors that may not be immediately apparent through traditional methods
Document Processing Automation: With tools like Azure AI Document Intelligence, the platform automates the extraction and processing of information from various documents, reducing manual workload and minimizing errors
Predictive Modeling: Utilizing machine learning capabilities, Azure AI assists in building predictive models that forecast risk and inform decision-making, leading to more accurate underwriting outcomes
The platform's scalability and flexibility allow for customization to meet specific organizational needs. Zurich Insurance Group has successfully implemented Azure AI to transform its underwriting processes, resulting in improved efficiency and customer satisfaction.
Surefyre is an AI-enabled, low-code/no-code submission and underwriting workbench platform specifically designed for Managing General Agents (MGAs) and wholesalers in the insurance industry. On November 4, 2024, Vertafore acquired Surefyre to enhance its AI capabilities and accelerate growth and innovation for MGAs.
Key Functionalities:
Configurable Agent Portal: Offers a modern, fully configurable agent-facing portal that supports the complete policy lifecycle
Underwriter Workbench: Enables MGAs to seamlessly rate and quote specialty Property & Casualty (P&C) business across systems
AI-Powered Solutions: Includes automated agent portal configuration and AI-driven features to streamline submission and risk selection
Artificial intelligence is improving insurance underwriting by automating tasks such as submission ingestion, risk assessment, and claims processing. Explore the detailed examples of AI for insurance underwriting to see how it can streamline operations and enhance decision-making.
hx Renew, developed by hyperexponential, is an advanced insurance pricing software tailored for complex risk decisions. The platform transforms pricing and underwriting processes through agile model development, streamlined workflows, and enhanced decision intelligence.
Key Functionalities:
Accelerated Model Development: Enables insurers to build, deploy, and refine pricing models up to ten times faster than traditional methods
Data-Driven Decision Making: Integrates powerful APIs and in-model management information for AI-driven pricing decisions
Workflow Integration: Connects with existing systems, reducing quote turnaround times by up to 50%
Aviva reported a 75% reduction in model build time after transitioning from Excel to hx Renew's modular framework. Convex has highlighted the platform's data reliability and advanced live insights.
Expert.ai specializes in natural language processing (NLP) to enhance various industries, including insurance. Their platform streamlines and automates complex processes by extracting and understanding information from unstructured data.
Key Functionalities:
Underwriting Automation: Automates tedious aspects of underwriting, such as reading and aligning policy content with checklists
Claims Processing: Unlocks data trapped in complex documents to automate end-to-end claims workflows
Policy Review and Comparison: Assists in comparing policy documents to identify discrepancies and ensure compliance
AI is not only improving insurance underwriting but also enhancing contract review processes. By automating the extraction and analysis of key clauses, AI enables insurers to assess policy terms more efficiently, ensuring compliance and reducing risks. Explore AI applications in contract review to see how these advancements can benefit your organization.
Gradient AI is a Boston-based enterprise software provider specializing in artificial intelligence solutions for the insurance industry. In July 2024, the company secured $56.1 million in Series C funding led by Centana Growth Partners.
Key Functionalities:
Underwriting Solutions: The SAIL™ platform leverages a vast industry data lake to evaluate potential submission costs
Claims Management: Predicts underwriting and claims risks, reducing quote turnaround times and claim expenses
Gradient AI serves various insurance providers, including group health insurers, property and casualty insurers, and workers' compensation providers. Their solutions enable accurate policy pricing, decreased quote turnaround times, and straight-through processing, improving loss ratios and profitability.
Integrations in AI Underwriting Software Explained
Implementing generative AI in insurance underwriting requires careful consideration of how new capabilities will integrate with existing systems. Most insurance companies already use established underwriting software platforms to handle core operations like policy management, rating, and documentation. Any new AI solution needs to work seamlessly with these systems rather than creating separate workflows.
The primary technical challenge lies in data exchange between systems. Modern underwriting platforms typically offer several integration options: APIs for direct data exchange, SDKs for custom integration development, and pre-built connectors for common underwriting software. Most AI providers support these standard integration methods—V7 Go, for instance, provides both REST APIs, Zapier, and custom integrations, while Multimodal offers database connections and API interfaces.
Cloud infrastructure plays a crucial role in enabling these integrations. When processing non-standard cases, AI systems need to access historical data from existing systems while returning insights in real-time. This is particularly evident in implementations like Zurich Insurance Group's use of Azure AI, where cloud infrastructure enables direct integration with their existing enterprise systems.
Analytics need to span both traditional underwriting metrics and AI performance monitoring. Real-time dashboards should track document processing accuracy, automation rates, and validation results alongside standard underwriting KPIs. This becomes especially important when handling complex cases that don't fit standard templates, where automated processes require careful oversight.
The goal is maintaining a single source of truth—whether the AI system is analyzing complex documentation, assessing risks, or suggesting pricing models, the insights need to flow back into the core underwriting system where decisions are documented and policies are managed. This explains why integration capabilities often determine implementation success more than raw AI capabilities.
Conclusion
In summary, the true value of AI in underwriting lies in its ability to handle non-standard, complex information. While automated underwriting systems like DU and LP are optimized for structured data, real-world underwriting often requires navigating unstructured market intelligence, technical specifications, and regulatory complexities.
Generative AI can bridge this gap by synthesizing diverse information and offering actionable insights to underwriters. Rather than replacing established systems, successful AI implementations complement them by focusing on judgment-intensive scenarios. This enables underwriters to tackle challenges that automation alone cannot address.
Schedule a demo today to see how AI can elevate your underwriting processes.
