How to Use Generative AI in Insurance for Improved Efficiency

The big takeaway: The application of generative AI in insurance agency operations improves claims processing, underwriting accuracy, and risk assessment, driving faster decisions and stronger client outcomes.

In brief:

• Generative AI in insurance accelerates claims processing, underwriting, and risk assessment by automating document review, fraud detection, and data analysis, reducing manual processing time and improving decision accuracy.
• Small language models (SLMs) make AI adoption more accessible for insurers by requiring fewer resources and delivering faster ROI on specific use cases without requiring cloud infrastructure or internet connectivity.
• GenAI enhances underwriting and pricing by analyzing complex multi-variable data sets (like fleet compositions or multi-location businesses) to generate accurate risk assessments and premium scenarios that would otherwise require hours of manual calculation.
• AI-powered risk monitoring systems provide early warning capabilities by continuously scanning market conditions, news events, and incident reports to identify emerging risk patterns before they impact profitability.

Every claim, policy, and risk decision depends on accurate information and consistent judgment. The application of generative AI in insurance improves both. It accelerates data processing, reduces manual review, and produces insights that make pricing and risk assessment more precise.

But, most insurers are still in early stages of AI adoption. The hesitation often comes from uncertainty about where to apply AI, how to measure AI ROI, and how to integrate AI into existing processes and systems.

However, insurance companies that invest in the implementation of practical generative AI applications will gain a lasting operational advantage.

Why Adopt GenAI Now?

The recent emergence of small language models (SLMs) is advancing AI adoption in the insurance industry, because SLMs — unlike the more familiar large language models (LLMs) — use fewer resources to tackle more specific use cases. That can make it easier to see results and prove ROI. Even so, left without guidance, insurers struggle with the uncertainty of using GenAI.

That’s why we compiled a list of practical applications of GenAI in insurance so you can feel confident implementing AI into your operations.

Examples of How Insurance Agencies Are Implementing Generative AI

Before diving into specific GenAI use cases, it’s important to note that all AI tools depend on clean, structured data. Before deploying AI across the firm, data teams should:

• Audit existing information and data to understand what is available, where it’s stored, and whether it’s complete and accurate.
• Eliminate duplicates and inconsistencies for reliable training and analysis.
• Tag and categorize documents consistently across systems for easier search and retrieval.
• Integrate data sources from different lines of business to create a unified view of clients and risk.
• Establish secure storage and access controls that meet regulatory standards.

Laying this groundwork is the best way to scale the use of GenAI effectively as you grow.

Use GenAI to Complement Predictive Analytics

Predictive analytics involves using historical data to predict future outcomes, which makes it a natural tool for insurance companies and any organization involved in risk assessment. Your team likely already relies on a form of predictive analytics to minimize the risk of setting premiums too high or too low.

However, many find the interpretation of insights provided by predictive analytics to be laborious, especially because there are many data points to consider. GenAI makes that interpretation effortless.

For example, you can upload a spreadsheet with historical claims data to a GenAI system and ask it which kinds of incidents are most likely to occur in certain areas or during different times of the year.

Some insurance risk management solutions make it even easier because they embed a GenAI system and function alongside their statistical predictive analytics engine. Instead of having to interpret the charts, graphs, and numbers you see on the screen for yourself, you can simply tap a button, and the GenAI system will summarize it for you.

In this way, generative AI in insurance hand-delivers vital decision-making information.

Use GenAI to Streamline Claims Processing

One of the most intuitive applications of generative AI for insurance providers is claims processing. Here are some examples of what insurance GenAI capabilities can do to support claims processing:

• Automatically recognize signs of fraud, such as multiple claims being filed for accidents at the same intersection
• Read emails and notes, and then automatically input that information into a claims form
• Identify fake images made using AI and then prevent fraudsters from profiting off false claims
• Automatically write reports and summaries of incidents, pulling vital information from the claims form and using it to create a narrative.

You can also pair GenAI with optical character recognition (OCR). OCR allows a computer to read text from a document and then digitize it into a text file. Therefore, an OCR system can take a raw PDF with claim information and digitize it into computer- and human-readable words.

Then, a GenAI system can use that data to complete a claims form automatically.

The security benefits of using GenAI are attractive too: claims data can be processed locally rather than over the cloud, making it easier to adhere to regulatory compliance, such as HIPAA rules, which leads to faster processing of routine claims.

Use GenAI to Simplify the Underwriting Process

Like many other data-dependent processes, underwriting also benefits significantly from GenAI integration.

For example, suppose an insurance company is preparing a package for a delivery business with a fleet of 30 vehicles. Each vehicle has a different age, condition, make, and model.

The good news? The company has carefully recorded all of its vehicle data.

The bad news? There’s a ton of it, and the underwriting department doesn’t have the time to sift through and make hundreds of calculations.

The great news is that GenAI can shoulder the heavy work. A generative AI system supports insurance underwriting by:

• Taking all vehicle data, collating it, and comparing it to industry norms
• Assessing the level of risk of each vehicle in the fleet
• Calculating different premium scenarios
• Incorporating historical data based on weather conditions and deliveries the company makes

This data-heavy analysis is done on-premise at predictable costs, instead of the variable pricing of cloud-based AI models that can spike during peak processing periods.

While it’s never wise to have AI make the final decision in an underwriting matter, it can do a lot of the legwork you need to help you decide on one choice or another.

Use GenAI for Optimal Pricing

Price-setting support is another way generative AI is being implemented in insurance firms. It helps optimize pricing strategies that make your products profitable and marketable.

For some clients, you have to wade through many factors before deciding on a price that fits both their budget and yours. But GenAI can handle this for you.

For example, a business client may have multiple locations, each with inherent risk factors. Ideally, the liability insurance cost for one location should be different from the others. The same goes for workers’ compensation, especially if one location tends to have more or fewer accidents than average.

Historically, balancing these factors can result in many hours hunched in front of a spreadsheet, but with GenAI, you can rest your brain and back. Generative AI can analyze all the data from each location and produce a single binder for the entire business that considers all factors.

It can also build individual binders for each business unit, so you can explain to the customer why their coverage played out the way it did. If you use SLMs, you can even do this analysis without an internet connection or relying on the cloud.

Use GenAI for Custom Product Development

Similarly, with product development, custom-designing products for each business can be difficult. But, in many cases, this is the best choice because each organization’s needs and operational environment are unique.

Insurance brokerage firms are using GenAI to help design custom coverage packages that reduce risk for both clients and the company.

To enable this at your firm, you can use your company’s offerings as the knowledge base for a GenAI system. The system can then create a package using a combination of your existing products.

You can also have the system create a completely novel insurance product from scratch. Then, with another prompt, you can have it describe how your company can create a similar package using elements of your current offerings.

However, it’s important to remember that AI is a stepping stone, not a final step, in the process of building attractive insurance offerings. Human insurance experts need to test, validate, and refine the product at every step to ensure it will work for your target market.

Even with ideal pricing and product development, you still may need help with the third leg of the insurance stool: risk assessment. Generative AI excels at this as well.

Use GenAI to Mitigate Risk

In addition to using predictive modeling to assess risk, GenAI-powered systems can act as early warning systems by:

1. Automatically monitoring the market for early signs of increased risk. For instance, GenAI can scour news headlines regarding weather and climate-related events in your target market.
2. Taking data and predicting the likelihood of incidents in potential clients’ locations. For example, weather-related events may affect farmers’ crops or the ability of transportation companies to operate efficiently. If your AI system reports high risk, you can nudge your prices up, which helps maintain solvency. On the other hand, if the risk is especially low, you can drop prices accordingly, giving your products more competitive price points.
3. Identifying elevated risk levels resulting from a series of events that, over time, culminate in expensive or dangerous incidents. For example, a construction company may hire a new manager who is less-than-stringent when it comes to promoting worksite safety. A GenAI system can analyze incident reports and inform you ahead of time of the degree to which the new manager raises the company’s risk profile.
4. Scouring through piles of reports is traditionally time-consuming and tedious. When done manually, it’s easy to miss things. But GenAI doesn’t get tired. It catches minute details that could make the difference between your company being in the red or black.

How to Put GenAI to Work for Your Insurance Company Now

Insurance companies are inundated with helpful data every day. Ignoring it or not tapping its full potential could harm your bottom line.

However, with GenAI tools custom-built for insurance firms, you can automate much of the tedious data reporting and analysis. And because these capabilities don’t have to be cloud-dependent, GenAI can immediately process data that resides on current hardware without requiring IT infrastructure changes or a cloud migration.

This results in profitable premiums, packages that better suit customers’ needs, and more accurate data analysis. While your company reduces risk for its clients, GenAI can reduce the risk for your company.

Connect with one of our insurance data consultants today for specific use cases on how to implement GenAI at your firm.

Insurance and GenAI FAQs

Answers to common questions about generative AI applications in insurance, including how they improve claims processing, underwriting, and risk assessment.

How is generative AI used in insurance?

Generative AI in insurance is applied to high-impact functions such as claims processing, underwriting, and risk assessments to help insurers make faster and more accurate decisions. These applications include:

• Automating document review
• Detecting fraud patterns and
• Summarizing complex data

What are the main benefits of generative AI for insurers?

The benefits of GenAI applications in insurance include:

• Improved operational efficiency
• Higher accuracy in data-driven tasks
• Stronger compliance
• Reduction in manual processing time
• Faster reporting
• Better pricing precision
• Easier product development

How can insurance firms mitigate the risks of using generative AI?

Insurers can mitigate risks by implementing strong AI governance frameworks, continuous monitoring, and human review of all AI-assisted outputs.

How should insurers measure ROI from AI applications?

Return on investment for generative AI in insurance can be measured by reductions in claims cycle time, improvements in underwriting accuracy, and lower administrative costs. Other metrics include policyholder satisfaction, automation rate, and measurable gains in data quality and compliance.

Are you ready to explore how artificial intelligence can fit into your business but aren’t sure where to start? Our AI experts can guide you through the entire process, from planning to implementation. Talk to an expert