Generative AI in Insurance: From Document Extraction to Agentic Workflows

Generative AI for Actuaries

Actuarial work occupies a different position in the AI adoption curve than underwriting. Where underwriters read submissions and make binding decisions, actuaries aggregate historical data and build models that inform pricing, reserving, and capital allocation. The work is already quantitative, but the data preparation remains stubbornly manual.

Generative AI attacks the data gathering and cleaning bottleneck that consumes 60-80% of actuarial time on most projects.

AI Use Cases for Actuaries

Here is how AI is reshaping specific actuarial tasks:

Data Extraction

Delegate the data extraction phase to a specialized AI actuarial agent. It reads financial statements, claims reports, policy documents, and regulatory filings to pull mortality rates, claims frequencies, policy counts, and other actuarial metrics. Your team gets clean, structured data ready for modeling—freeing actuaries to focus on assumptions and valuations.

Regulatory Reporting Automation

Statutory filings require data from multiple sources, formatted according to specific requirements that change annually. An agent can pull data from your policy and claims systems, apply the current year's reporting rules, generate the required schedules, and flag items that need actuarial review. What used to take a team three weeks now takes three days, with the actuaries spending their time on the exceptions rather than the routine calculations.

Experience Study Preparation

Pricing reviews require detailed experience studies—breaking down claims by coverage, geography, industry, policy size, and other dimensions. An agent can extract this data from your systems, calculate loss ratios and frequency/severity metrics, identify trends, and generate preliminary rate indications. The actuary reviews the results, adjusts for known issues (large losses, exposure changes), and finalizes the recommendations.

Reinsurance Treaty Analysis

Understanding your reinsurance program requires reading treaty wordings, tracking cessions, and modeling various loss scenarios. An agent can extract treaty terms from reinsurance agreements, identify coverage gaps or overlaps, and simulate how different loss patterns would impact your net retention. This is particularly valuable for complex programs with multiple layers and facultative placements.

Generative AI: The Impact on Actuaries

Actuarial work is already highly technical. The impact of AI is not about replacing actuaries, it is about letting them focus on the parts of the job that require actuarial judgment rather than data wrangling.

• Less time in Excel, more time on model assumptions. When data aggregation is automated, actuaries can spend more time thinking about whether their assumptions are reasonable. Is the trend factor appropriate given recent market changes? Should we segment this class differently? Are we adequately capturing the impact of climate change?

• Faster iteration cycles. When you can regenerate an analysis in hours instead of weeks, you can test more scenarios. What if we exclude the top 5% of claims? What if we adjust for exposure growth differently? What if we use a different development pattern? The ability to iterate quickly leads to better models.

• More sophisticated analyses become feasible. Some analyses are theoretically valuable but practically impossible because they require too much manual work. With AI handling the data preparation, you can analyze claim development patterns by adjuster, compare reserve adequacy across different underwriting teams, or model the impact of specific policy language changes.

• Junior actuaries learn faster. When you are not spending your first two years copying data between spreadsheets, you can focus on understanding the business. You can work on real pricing studies, reserve analyses, and capital modeling—the work that builds actuarial judgment.

AI for Claims Processors

Claims generates some of the highest ROI from generative AI because the work is fundamentally about interpreting evidence. Claims processors read FNOL reports, adjuster notes, police reports, medical documents, estimates, invoices, and policy wordings. They synthesize that information against coverage terms, regulatory requirements, and historical patterns. Then they make a decision: pay, deny, or negotiate.

95% of claims handlers are confident technology will significantly impact claims processing in the next five years. The report shows that contrary to common fears about artificial intelligence (AI) and automation displacing human workers, claims handlers see technology as an opportunity to improve processes, both for themselves and their customers.

International Travel & Health Insurance Journal

AI Use Cases for Claims Processors

Here is how AI is reshaping specific claim processing workflows:

FNOL Intake and Triage

An AI Claims Triage Agent receives a First Notice of Loss and immediately extracts key data points: date of loss, cause of loss, parties involved, policy number, location, and preliminary severity indicators. It checks this against coverage triggers and regulatory reporting thresholds. The agent flags issues requiring immediate escalation—potential fraud indicators, bodily injury, statutory deadlines. Claims handlers see a clean, structured summary instead of raw, inconsistent intake data.

Document Review and Evidence Extraction

Policies, medical records, police reports, repair estimates, and invoices can run hundreds of pages. An Insurance Claims Automation Agent processes them in seconds. It can highlight inconsistencies—treatment codes that don’t match injury descriptions, repair invoices exceeding regional averages, or conflicting statements within the file.

Policy wording is also often complex, especially with endorsements and jurisdiction-specific requirements. An agent can compare the facts of the loss against the policy in force, identifying applicable coverage sections, exclusions that may apply, and ambiguity points requiring human review.

Damage Assessment and Repair Validation

Inspection photos and adjuster field notes often contain crucial details buried in images and free text. An AI agent can analyze photos to confirm damage type, compare against pre-loss images, and detect potential pre-existing conditions. It cross-references estimates with labor rates, parts databases, and historical claims to identify overbilling or unnecessary repairs.

Fraud Pattern Detection

Fraud detection requires spotting subtle inconsistencies across documents, timelines, and claimant histories. An AI Fraud Detection agent can correlate narrative inconsistencies, unusual provider billing patterns, repeated vendors across unrelated claims, or suspicious timing sequences. Instead of a simple fraud score, it generates structured reasoning—“two prior losses with same repair shop, injury severity inconsistent with medical records”—and surfaces them for human review.

AI: The Impact on Claims Processors

For decades, claims processors have been forced to function as administrative routers, moving data between PDFs and legacy systems while trying to maintain the mental bandwidth to negotiate settlements. Generative AI fundamentally alters this dynamic

“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.”

— Trent Services

• From "Processors" to "Problem Solvers": When the FNOL is auto-populated and coverage is pre-verified, the adjuster picks up the phone already knowing the facts. They can focus on empathy, negotiation, and strategy—the human skills that drive Net Promoter Score (NPS) and fair settlement outcomes.

• Higher capacity without burnout: The administrative burden of claims is a major driver of industry turnover. AI relieves this cognitive load.

• Reduced leakage through precision: A human claims processor, under pressure to close files, might rubber-stamp a $500 supplement or miss a subtle pre-existing condition. An AI agent has infinite patience. It checks every line item against labor rates, parts databases, and historical photos. It stops the "death by a thousand cuts" of claims leakage that humans simply don't have the time to catch.

• Junior adjusters upskill faster: The traditional path for a new claims processor involves years of data entry and simple file management. With AI handling the intake and basic liability checks, junior staff can be exposed to complex decision-making earlier. They spend less time learning how to use the claims system and more time learning how to assess liability and negotiate settlements.

Learn more about AI claims processing here: Automated Claims Processing: Implementation Guide for Insurance Operations

Generative AI for Brokers

Brokers are client-facing, relationship-driven, and constantly juggling multiple accounts at different stages of the insurance lifecycle. Their bottleneck is often handling high volumes of routine requests while still providing personalized service.

The efficient use of AI to streamline those processes which can be easily and appropriately automated can then enable brokers to find more time to focus on engaging with their customers and providing the tailored advice they require.

—Mills & Reeve

AI Use Cases for Brokers

Here is how AI is reshaping specific broker workflows:

Certificate of Insurance (COI) Requests

This is a common pain point. A client needs a COI for a vendor, landlord, or contract requirement. The broker has to pull the policy, verify coverage, generate the certificate, and send it to the client. For a large account, this might happen 50-100 times per year. An AI COI automation workflow handles the entire process: receive the request via email, verify coverage against the policy, generate the certificate with correct language, and send it to the client—all without broker involvement unless there is an issue.

Policy and Quote Comparisons

When shopping coverage, clients want to understand the differences between proposals. An agent can analyze multiple quotes, extract coverage terms, identify differences in limits, deductibles, and exclusions, and generate a comparison summary. Instead of the broker spending an hour creating a spreadsheet, the client gets a clear comparison in minutes.

Renewal Preparation

Sixty days before renewal, an agent can pull the current policy, recent claims, any mid-term changes, and market conditions. It generates a renewal strategy memo: "Premium is likely to increase 8-12% due to two claims and market hardening. Recommend increasing deductible from $5K to $10K to offset. Also suggest adding cyber coverage given client's increased online sales." The broker reviews and refines, but the groundwork is accelerated.

Client Communication and Follow-Up

An agent can monitor email for client questions, draft responses based on policy terms and prior conversations, and flag items that need broker attention. Simple questions ("What is my deductible for water damage?") get immediate answers. Complex questions ("Will this cover our new manufacturing process?") get flagged for broker review with relevant policy language already pulled.

Common Pitfalls for Generative AI in Insurance

Most AI implementations fail not because the technology does not work, but because organizations misunderstand what they are buying or how to deploy it. Here are the patterns that lead to failure.

Pitfall 1: Treating AI as a Black Box

Insurance is a regulated industry, and you can't make decisions based on systems you do not understand. When an underwriter declines a risk, they need to explain why. When an actuary sets a rate, they need to justify it to regulators. "The AI said so" is not an acceptable answer.

The solution is explainability. Every AI decision should come with reasoning and evidence. V7 Go's visual grounding provides this—when it extracts a data point, it shows you the source.

Pitfall 2: Expecting Perfect Accuracy

No AI system is 100% accurate. LLMs hallucinate. OCR misreads text. Models make mistakes. If you deploy AI expecting perfection, you will be disappointed and your users will lose trust.

The solution is human-in-the-loop workflows. AI handles the bulk of the work, but humans review exceptions and edge cases. For example, an underwriting agent might auto-route submissions that clearly meet guidelines, flag borderline cases for review, and auto-decline obvious mismatches. The underwriter focuses on the middle category—the cases that require judgment.

Pitfall 3: Ignoring Data Quality

If your historical data is messy, inconsistent, or biased, your AI outputs will be too. Garbage in, garbage out.

Before deploying AI, audit your data. Are claim codes consistent? Are policy wordings standardized? Are underwriting decisions documented with clear reasoning? If not, you need to clean your data first. This is tedious work, but it is necessary.

The good news is that generative AI can help with data cleaning. An agent can identify inconsistencies, suggest corrections, and flag anomalies. But you still need human oversight to validate the corrections.

Pitfall 4: Underestimating Change Management

The technology is the easy part. The hard part is getting people to use it. Underwriters who have spent 20 years reading submissions manually will not trust an AI summary without proof that it works. Actuaries who have built their careers on Excel models will not switch to AI-generated analyses without validation.

Successful implementations start small. Pick one workflow, prove it works, and expand. For example, start with COI automation for brokers. It is low-risk, high-volume, and easy to measure. Once brokers see that it works, they will trust the system for more complex tasks.

Also, involve users in the design. Do not build an AI system in isolation and then force people to use it. Work with underwriters, actuaries, and brokers to understand their workflows and pain points. Build solutions that fit how they actually work.

Generative AI adoption follows the typical hype cycle, early enthusiasm, followed by disillusionment, then gradual productivity gains.

Pitfall 5: Focusing on Cost Savings Instead of Value Creation

The ROI pitch for AI is usually "we will reduce headcount by automating manual work." This is shortsighted. The real value of AI is not replacing people, it is enabling them to do higher-value work.

An underwriter who spends less time on data entry can write more business, handle more complex risks, and build stronger broker relationships. An actuary who spends less time on data preparation can build better models and provide more strategic insights. A broker who spends less time on COIs can focus on risk management consulting.

Measure AI success by business outcomes, not cost reduction. Are you writing more profitable business? Are you retaining more clients? Are you entering new markets? These are the metrics that matter.