This is arguably a golden age for leveraging technology in the insurance industry, with artificial intelligence use cases driving much of the current focus. AI is revolutionizing every step of the insurance process, from marketing, to underwriting, to claims to detecting fraud, potentially transforming the way we work.
We’re still in the early stages of the AI boom and only beginning to grasp how it will be woven into the fabric of our lives. Use this FAQ/guide of essential AI terms and applications to gain a better understanding of the technologies you’ll likely run across in your AI journey.
First off, what is AI? According to IBM, “Artificial intelligence, or AI, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.”
There are many types of AI, but for the purposes of this article, we’ll focus on two of the more common ones. While so-called traditional AI has been developing since the 1950s, much of the current focus is on generative AI which is becoming widely available.
What’s the difference between traditional and generative AI?
Traditional AI is the more structured rule follower of the two, while generative AI is its creative cousin.
Traditional AI shines at problem solving and can be used for specific tasks, such as decision trees, analyzing data to reach conclusions, or even editing out awkward pauses and “ahs” and “ums” in that video you just recorded for a client video proposal.
Using a simple request, Generative AI can create original content such as text, music, pictures, and video. Common text-based Generative AI models include ChatGPT, Claude from Anthropic, and Microsoft Copilot. Frequently used image-based AI models include Dall-E2, Midjourney and Vivid AI. There are many others out there, including a wide range of AI audio and AI video generators.
Today, many insurance agencies are using generative AI to create the first draft of marketing content, images for blogs, and initial cuts at client email responses. Other AI tools summarize key aspects of policy documents in non-insurance speak to share with clients.
How does Natural Language Processing (NLP) fit into this?
Natural Language Processing is a subset of artificial intelligence that can understand, interpret, and generate human language. NLP helps our new AI overlords sound more human when responding to typed text or spoken voice queries.
You may come across NLP in customer support systems. For instance, if a customer asks about the details of their insurance policy, an NLP-enabled AI can comprehend the question and respond with relevant policy information.
Where does AI get its information from?
Various AI models can analyze vast amounts data, including text, images, audio, video, and programming code. It may pull it from the internet, databases, or that private customer information you’re about to feed into ChatGPT. NOTE: Don’t do that.
That data allows AI to provide detailed responses in text or voice, images, audio and video.
Here’s an important caveat: AI may seem smart, but always check what it creates.
AI can get it wrong?
Yes. AI can create inaccurate or misleading answers. The data used to train it might be incorrect or have biased information. Or it might make assumptions when asked a question lacking context and jump to an incorrect conclusion (how human!).
AI aims to please and will try to provide an answer, even if it turns out it’s not correct. Researchers call these hallucinations.
Hallucinations in AI are instances where responses are not based on actual data or reality. What may appear credible can be fabricated or downright misinformation.
Real-world examples of AI hallucinations include the lawyers who submitted fake legal cases made up by ChatGPT. And image-generating tools have famously struggled with getting the number of fingers on hands correct.
What’s an AI chatbot?
AI chatbots use conversational AI models designed to engage in human-like dialogue. It can be in writing, or using a voice, such as with chatbots like Amazon’s Alexa, Apple’s Siri, Google’s Gemini and others that can understand your voice and respond in kind.
Think of a chatbot as a knowledgeable customer service agent who is always available and never needs a coffee break.
For example, a client could ask your agency’s chatbot, “what’s my deductible?” Or “what’s covered under my business owner’s policy?” The chatbot would then answer using data provided by the agency to the bot or advise the customer where they can find the information.
Is Machine Learning the same as AI?
Machine Learning (ML) is a subset of artificial intelligence that uses algorithms and statistical models to enable computers to learn from and make decisions based on data. Unlike traditional programming, where explicit instructions are provided for every task, machine learning algorithms can identify patterns and insights from data to improve over time.
Example: In the insurance industry, machine learning can be employed to assess risk and price policies more accurately. By analyzing historical data, an ML model can predict future claims and adjust premiums accordingly, providing more tailored pricing for customers.
What is an Algorithm?
An algorithm is essentially a set of rules or instructions – like a cookbook recipe – designed to solve specific problems or perform tasks. Just as a recipe describes the steps needed to make pancakes, an algorithm describes the steps required to accomplish a task, such as analyzing claims data for next steps or potential fraud.
How would AI read claims and other documents?
One way is using OCR, or Optical Character Recognition – a technology that can convert scanned paper documents, PDFs, or digital images, into data that can be searched and edited.
How do I ask AI what I want it to do?
Prompts are used to guide AI in generating responses or performing actions. Prompting for AI tends to follow a general structure that might sound similar to a conversation you’d have with a coworker.
Say you have a task for your eager intern Fern to create some website content. Depending on the complexity of the task, you may want to describe her role in the process, the task, provide context, requirements, boundaries, and ask her to explain her reasoning or approach for creating the content. You might use only a couple, or all six components in your prompt.
“Fern, I’d like you to think like an SEO expert (role) and create a blog post that would help customers understand why they’d want to buy insurance from an independent agent (task). Our core customers are homeowners who also own businesses within 25 miles of our agency (context). Write roughly 800 to 1,000 words in a professional and approachable tone similar to our other blog posts (requirements). Let me know how you plan to do the work (reasoning).”
Depending on what Fern comes back with, you might pose additional prompts, such as ones asking her to create social posts or an image to go along with the blog.
Whether you asked Fern or used generative AI to write that blog post, treat it like a first draft, making adjustments for accuracy, style, tone, and clarity. Everyone, even AI, needs an editor.
What other similar technology is being used in insurance agencies? Exploring Robotic Process Automation (RPA)
Robotic Process Automation, or RPA, refers to the use of software to handle highly repetitive and often tedious tasks that humans do, like data entry. That human time saved can be repurposed to more fulfilling work.
For example, some insurance agencies use RPA to convert the filenames of downloads to a consistent naming convention, then file them in the appropriate locations. Or, RPA can automate the process of policy administration. By accessing multiple systems, validating information, and executing the renewal process without human input, RPA can significantly reduce processing time and improve accuracy.
What’s next with artificial intelligence in the insurance industry?
While this is a brief look at a rapidly evolving artificial intelligence landscape, it will be updated over time as we begin to see more concrete impacts on the insurance industry.
In the meantime, what other AI terms and topics do you want to learn more about? Post your ideas in the comments section below.
