Following a major catastrophe, insurers usually get slammed with thousands of new claims—each packed with documents and photos—and their switchboards get flooded with questions from policyholders. Pre-AI, sorting it all could take weeks before an adjuster got involved. But today, AI can read, triage, and route claims to an adjuster in minutes.
That scenario is possible right now, but for most carriers, it’s still just a possibility. Insurers outpace nearly every industry in AI adoption—save for technology, media, and telecom. But only 7% have successfully scaled their AI pilots, which frequently stall amid organizational hurdles, including concerns about the impact on the rest of the company.
Capgemini Research says it has found a blueprint for scaling AI: “intelligence trailblazers”—insurers that align AI with strategy, talent, technology, and adoption. The researcher found that these organizations experienced up to 21% higher revenue growth and roughly 51% greater share-price growth than mainstream insurers from 2021 to 2024. This article provides practical insights for insurers looking to achieve similar benefits.
What Is AI in Insurance?
AI in insurance refers to software that finds patterns in claims and policy data that help insurers gauge risk and automate the document-heavy work behind underwriting and claims decisions.
It’s a broad term that covers a mix of technologies, including machine learning, computer vision, natural language processing, and generative models. AI tools are largely delivered through the cloud. AI emerged in the insurance industry in the 1990s, with predictive modeling tools that are now widely used for pricing, risk selection, and fraud scoring. Generative AI followed in 2023 and quickly became commonplace for reading, writing, and summarizing tasks. The newest arrival, AI agents, can carry out multistep tasks across multiple systems independently, while certain final decisions are reserved for human review.
Key Takeaways
- Many auto, home, and life insurers use, plan to use, or are exploring AI, but adoption varies by line of business and few have scaled AI beyond pilots.
- US state regulators are adopting the NAIC’s Model Bulletin on the Use of Artificial Intelligence Systems by Insurers, which sets expectations for responsible AI use, governance, and data practice.
- Claims processing is a clear AI proving ground, exemplifying how AI-assisted intake and triage are paying dividends during catastrophe-heavy periods.
- Fraud-detection is another standout insurance AI use case; fraudulent losses are mounting, and AI’s knack for pattern recognition gives investigators an edge.
- Automation works best when people remain in charge to protect customers’ trust.
AI in Insurance Explained
When most insurance executives think about AI, they probably picture a customer-facing chatbot. But today, that’s just a small part of the story. Peek under the hood of insurance operations and you’ll find AI playing a larger role in places customers don’t see, such as underwriting, compliance, and governance. AI profoundly changes day-to-day operations, and when it’s wired directly into underwriting, claims, and accounting processes, the payoff becomes evident. BCG’s work with US and UK commercial P&C insurers suggests AI can improve underwriting efficiency in complex lines by up to 36%.
As insurers embed more AI into their operations, US regulators are pushing for stronger accountability. AI oversight runs through state insurance departments, which are increasingly aligned with the NAIC’s Model Bulletin, which more than 20 states have adopted. It calls for carriers to document how their AI systems are built and tested, guard against unfair discrimination in their models, and take responsibility for outcomes, even when third-party tools are involved. Everything starts with data governance: Weak data or loose controls lead to indefensible AI decisions when auditors come asking.
How Does AI in Insurance Work
Insurance applications usually combine several types of AI. Knowing what each one does makes it easier to understand what a given tool can deliver:
- Optical character recognition (OCR): OCR converts anything printed or handwritten (such as faxed medical bills or mailed-in claim forms) into data that software can use. Without OCR, most of an insurer’s incoming paper would never reach an AI model.
- Computer vision: Reads images and video the way an adjuster’s eye would. For example, gauging roof damage from a drone shot or spotting the same “wrecked” car in two unrelated claims. The primary benefit is faster damage estimates.
- Natural language processing (NLP): Insurance staff spend inordinate hours reading policy documents and transcribing claim calls. NLP can take much of that work off their plates by reading accounts of an accident, pulling key terms from a policy document, or summarizing recorded calls.
- Application programming interfaces (APIs): APIs connect AI with the insurer’s systems of record for policy, claims, billing, and CRM, so AI tools can work with current business data.
- Predictive analytics: Predictive models analyze historical patterns to forecast future probabilities—say, the chance of a policy lapsing at renewal or the likely cost of an open claim. They’re the engine behind current approaches to insurance pricing and risk selection.
- Business process automation (BPA): Although BPA itself is not a type of AI, an insurer’s existing BPA-driven automations can spoon feed their data to newer agentic AI-governed business processes. BPA has traditionally been the connective tissue between one task and the next. For example, when it comes to the insurance industry, it has traditionally orchestrated rule-based routing, such as kicking off the next step after a claim is approved or escalating a stalled file.
- Generative AI: Underwriting submissions and claim files are, to be generous, dense. GenAI analyzes and summarizes them in seconds using plain language. It can also draft documents, such as coverage summaries and explanations of denials, in terms a customer understands. That’s hours of work taken off employees’ plates.
Benefits of AI in Insurance
The business case for AI in insurance rests on four measurable outcomes: faster work, sharper customer outreach, capacity that scales, and less fraud. They’re also the outcomes that define the “intelligent trailblazers” mentioned earlier:
- Better productivity: Insurers usually produce their first AI gains when applying workflow automation to repeatable work—catching a duplicate invoice or tagging an incoming claim by type. The true benefit is allowing staff to use their expertise where it’s most needed. AI can also improve customer service productivity, a customer relationship function where policyholders often take notice.
- Increased personalization: AI means insurers don’t have to rely on one-size-fits-all customer messaging. For example, AI can detect a coverage gap and draft a message with a helpful suggestion. Timely and relevant messaging can help to win renewals.
- Enhanced scalability: AI tools don’t take breaks and can handle high-volume work at a fraction of the cost of full-time employees. When a storm season overwhelms claims-intake teams, AI can read, route, and triage documents.
- Improved fraud detection: AI can catch more suspicious claims while clearing honest ones faster. The search for a few bad actors doesn’t impact the experience of legitimate customers.
10 Use Cases of AI in Insurance
Adopting AI throughout an insurance operation creates a prioritization challenge. There’s no shortage of places for AI to help but no carrier can pursue every use case at once, and picking the wrong starting point can be costly in terms of both time and money. The priority depends on which use cases are most urgent.
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Claims Processing Automation
In the first three weeks following the 2025 Los Angeles wildfires, the California FAIR Plan, the state’s insurer of last resort after many companies left the California market, received 4,400 claims. Extreme situations like this are where AI shines. It can log first notice of loss, pull details from submitted documents, triage by severity, route files to adjusters, suggest reserves, and draft the first letter to a policyholder. Every hour or dollar saved on a single case becomes significant when multiplied across all claims arising from a major catastrophe.
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Fraudulent Claims Detection and Prevention
Fraud is a natural target for AI because it can spot patterns human reviewers may miss. A model can highlight discrepancies that suggest a padded claim, for example, or uncover a connection among claims that share phone numbers, pointing to a possible organized ring. And the stakes are climbing: Fraudsters are using GenAI to create convincing fake documents to fool adjusters and detection models.
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Underwriting
Underwriting is judgment work that’s often buried under a pile of paper—loss runs, applications, medical records, and prior files to review before a decision can be made. AI can clear a lot of that paperwork by summarizing documents and pointing to risk signals worth a second look. It can also check a submission against a carrier’s risk appetite before anyone opens the file. The underwriter still owns the decision, but the process starts with the pre-reading already done. AI agents can play an earlier role by collecting missing evidence and sorting the submission queue.
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Customer Service Automation
AI’s real value in customer service often happens behind the scenes. It can suggest answers, capture call notes, and walk customer service reps through common requests, then step aside when human involvement is warranted. Striking the right balance between AI and human involvement can drive improved loyalty, therefore turning customer retention metrics into a useful measure of customer service AI.
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Intelligent Document Processing (IDP)
IDP combines the reading and language technologies discussed earlier to turn stacks of paperwork, including applications, endorsements, loss reports, medical records, and contracts, into usable information for underwriters and claims managers. OCR and GenAI can reduce reading time by pulling facts from documents and flagging missing details.
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Distribution Productivity
For all the benefits of automation, commercial insurance, in particular, still relies on agent relationships, making agent productivity a priority for insurers. Agents spend valuable time on housekeeping tasks, like keying in submissions and looking for the carriers to write a given risk. AI can reduce that workload by pulling submission data directly from account documents and matching risk profiles against carrier preferences.
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Customer Retention and Personalized Engagement
Customer retention is a familiar industry priority. AI can support retention by recognizing early signals that suggest an impending lapse or defection—a service complaint, perhaps, or a quote pulled elsewhere. Beyond such early warnings, AI models can suggest outreach approaches that are most likely to work.
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Regulatory Compliance
Regulators that have adopted the NAIC’s Model Bulletin on the Use of Artificial Intelligence Systems by Insurers expect carriers to document AI governance and controls and address the risk of unfair discrimination. Maintaining that documentation is an ongoing burden that AI can help manage. It can monitor models continuously, compile evidence for examiners, run bias tests on a set cycle, and generate plain-language explanations in ways that make compliance recordkeeping more consistent and repeatable.
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Workforce Productivity
AI can improve day-to-day productivity. For example, it can pull up policy details mid-task for an employee and draft a customer email or claim note. But AI tools alone can only go so far. Teams are less likely to welcome AI if they lack training, an understanding of the design process, or clear communication about how roles will change.
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Billing, Collections, and Payment Reconciliation
Every policy and claim represents a stream of money that must be tracked, matched, and closed out. Handling invoices, agency payments, receivables, disbursements, and commissions may seem unglamorous, but it’s where an AI-equipped ERP system can deliver immediate benefits. It can reconcile payments and pinpoint exceptions that require human input, leaving a clean audit trail.
Emerging Trends in AI Technology for Insurance: AI Agents
AI agents are grabbing headlines, and for good reason. They have significant potential in insurance because they can independently and proactively manage many multistep, multisystem tasks that insurers handle every day. Take an incomplete claims submission: Agents can recognize what’s missing, request documentation, pull supporting data from other systems, assemble the file, and hand an underwriter a decision-ready package—all on their own, using other software in the process. Some AI agents can use memory to learn from prior interactions and adapt their behavior.
That said, the degree of autonomy afforded to AI agents is currently limited. Most insurance AI runs with a person accountable for the outcome. The realistic near-term benefits are modest but useful, as agents stitch together the steps of a process while humans supervise the results. For now, the smart move is to clean up data and tighten governance so everything’s ready for the day an AI agent can be trusted to run an entire process from start to finish.
ERP Software: The Foundation for Successful AI Adoption
Unsuccessful insurance AI projects frequently trace back to the same root cause: the underlying data. NetSuite ERP Software for Financial Services consolidates financial, operational, billing, and workflow data into one governed system of record and links AI outputs to approvals and audit trails, so carriers can defend decisions when state examiners ask.
NetSuite’s AI capabilities run natively on that same governed data. AI-powered Invoice and bill capture speed reconciliation, anomaly detection spots irregular claims and billing entries, predictive planning builds sharper forecasts, and narrative reporting delivers financial results in plain language.
Picture an overworked claims team the morning after a rush of claims. What allows them to dig out from those claims isn’t a clever algorithm. Rather, it’s AI embedded into workflows, drawing on trustworthy data, with employees making the final decisions. AI technology itself is fast becoming a given in the insurance industry and the carriers that are most successful with it are distinguished by the accuracy of their data, prioritization of projects, and attention to governance.
AI in Insurance FAQs
How is AI shaping the insurance industry?
AI is taking on time-consuming insurance administrative work—sorting claims documents or summarizing submissions, for example. With that work off their plates, underwriters and adjusters can spend their time on judgment calls that require their expertise. AI ultimately compresses timelines that once took days or weeks into hours or minutes.
How is AI used in insurance?
Common AI use cases in insurance range from claims intake and triage to fraud detection and reconciliation. It’s popular in claims, where it triages incoming losses and drafts basic correspondence, and in underwriting, where it summarizes submissions and identifies risk signals.
What are the challenges of implementing AI in insurance?
Most of the difficulty involves data, ownership, skills, and oversight. Many insurers have aging core systems that don’t share data cleanly, as well as unclear ownership of AI initiatives and no established measures for returns on AI investments. Those issues are compounded by a shortage of AI talent, hard questions about potential AI bias, and heavy reliance on outside vendors.
