The insurance industry is experiencing a seismic event with the emergence of generative AI, a powerful tool that can help insurance agencies streamline processes and improve their operations. Generative AI automates repetitive tasks and serves as a virtual assistant of sorts for insurance agents.
However, there are several pitfalls to avoid when using generative AI in the insurance industry. We’ll cover those in detail below while outlining which AI tool to choose and how to train it for your specific use case.
Generative AI Use Cases
Generative AI is everywhere nowadays, and it can help foster product development, data analysis, risk assessment, and marketing for insurance agents. Let’s dive through some top generative AI use cases and examples of each:
- Data Analysis: AI can help build accurate policy pricing and risk forecasting models. These models must be fine-tuned and tested, but generative AI makes the process less daunting. On a smaller scale, AI can summarize and look for patterns when handed a large dataset, helping to provide answers and sharpen decision-making accuracy.
- Customer Service: You have likely encountered an AI chatbot before, and insurance agencies are using it as an efficient way to answer questions, capture leads, and provide quotes to potential customers. This allows insurance agents to immediately respond to potential customers and scale their inbound/outbound marketing efforts.
- Social Media & Marketing: AI can be used to create and repurpose content for various social media channels. This includes creating videos, writing blog posts, helping construct marketing materials, and more. In other words, it cuts down the time it takes to keep an active social media presence.
- Personalized Communication: In addition to social media, AI can help craft personal messages and emails faster with less effort. Once again, this is all about making insurance agents more efficient in their work while using less energy and time on smaller tasks.
Generative AI for Insurance: Best Practices for Insurance Agents
There are several AI tools on the market right now. Some of them are free and others have premium subscription options that provide more usage and customization.
We’ll run through some popular generative AI choices in the section below, and cover some best practices for insurance agents to consider when utilizing these tools.
Choosing the Right AI Tool
ChatGPT is the most popular generative AI tool on the market right now. Insurance agents can use it for a wide range of purposes, including the use cases noted above. There is a free version that comes with certain daily usage limits. Otherwise, the paid version for a monthly fee has no usage limits and provides access to the most up-to-date model.
Claude is very similar to ChatGPT and has a free version with usage limits. A premium paid subscription will waive those restrictions and grant access to a more robust model. Each model has strengths and weaknesses, and it’s recommended that you play around with both to see which tool best suits your needs.
Perplexity is another generative AI tool that is similar to ChatGPT and Claude, and like them, offers free and paid models. It combines web search capabilities with conversational AI to deliver instant answers with cited sources that make verifying and checking work easier.
Each tool, as well as many other generative AI tools on the market today, has its own advantages and drawbacks. For example, ChatGPT might work great for analyzing and organizing data, while Claude could be considered a better option for writing social media posts, and Perplexity best for generating blog posts.
Experimenting and iterating these broad AI tools makes sense for insurance agents who want to improve the efficiency of their operations through generative AI.
Checking for Accuracy
No matter which AI tool you are using, you’ll need to double-check the output for accuracy. These platforms have come a long way in the past few years, but they are still error prone. The last thing you want is to make decisions based on faulty data conclusions or create a blog post with the wrong advice.
Sometimes, AI tools tend to “hallucinate” and make up “facts.” When the tool can’t find the correct answer, it latches on to the next most logical conclusion. If it does this enough times consecutively, the AI tool will “go down the wrong path” and produce an output that doesn’t make sense or is simply incorrect.
However, the tools always present their output confidently, as if they were presenting accurate information, so it’s always best to check the work product.
Avoiding Unintentional Plagiarism
It can be helpful to have AI assist you when creating content, but essential to not allow it to do 100% of the work for you. On top of the accuracy issues noted above, you could unintentionally run into plagiarism issues if you simply copy and paste content produced by ChatGPT, Claude, Perplexity, or any other AI tool.
These AI tools learn from different sources to help produce their output. Sometimes, it will borrow ideas that have been posted elsewhere. If it does not change the wording or occasionally borrows a proprietary idea without giving appropriate credit, it could result in a charge of plagiarism.
This is another reason it’s important to double-check the output of AI tools and fact-check or cross-reference the claims or information produced.
How to Train AI to Work Best for Insurance Companies
It makes sense for companies conducting business in the insurance space to train, test, and iterate AI tools before using them to create or make decisions. To effectively train AI, insurance businesses should start by feeding it relevant datasets. Here’s a general overview of how the process should look:
- Create diverse datasets and feed them to AI. This will help improve the accuracy of the AI model. Give it past datasets and test its knowledge to see if it arrives at the same conclusions. Then, point out which patterns or references are useful for the tasks moving forward.
- Monitoring performance. AI is far from a “set it and forget it” tool. You’ll want to consistently monitor the AI's performance and double-check its output. As mentioned earlier, one erroneous data set or variable can send the tool down the wrong path and lead to errors.
Examples of Trained Generative AI for Insurance
It’s possible to train an AI tool to help spot false claims and detect fraud. For instance, a Third Party Administrator (TPA) could present AI with past fraudulent claims, make it aware of key indicators, and train it to detect certain patterns.
From here, testing the tool with known fraud cases makes sense to see if it can catch the needle in the proverbial haystack. It’s imperative to have human oversight working in coordination with these tools, but generative AI could be used as a screening tool for detecting potential fraud cases for further review.
Streamline Your Insurance Compliance
At 3H Corporate Services, we provide powerful software solutions to help streamline insurance operations. One such solution is our Compliance Management Software, which manages corporate filings and insurance licenses.
This saves your insurance business time, headaches, labor costs, and errors by having a centralized system that accurately tracks and provides the status of these filings.
You can learn more about our services and software by contacting us with any questions.
