How Generative AI Will Impact the Insurance Industry
Many are calling 2024 the “year of AI.” As machine learning technology rapidly develops and becomes widely available, AI—artificial intelligence—will inevitably impact every industry and everyday life.
Generative AI’s use in producing text, images, and audio is already widespread. So, what will happen when it catches on in the insurance industry?
What is Generative AI?
Generative AI (sometimes shortened to “gen AI”) is defined as the type of AI that can produce content in the form of text, images, audio, or other mediums. Think of ChatGPT writing articles, the AI-produced art you may scroll past on Facebook or Instagram, and the AI-generated song covers you might hear on YouTube.
This content produced by generative AI is often indistinguishable from that created from scratch by real humans. This can be scary, but it also means that AI is already incredibly useful.
Beyond artistic and written content, generative AI can also be used for more analytical purposes. It can create predictive models, synthesize information gathered from multiple sources, and detect anomalies in datasets. In these uses, AI can go beyond our own capabilities and reduce bias and human error, if used correctly.
It’s already being employed in the insurance industry. A recent survey by Celent found that half of insurance companies had tested using AI by the end of 2023, and over a quarter had made plans to start using it by the end of 2023.
How Can Insurers Use Generative AI?
There are a variety of purposes for generative AI in the insurance industry, ranging from marketing and customer service to fraud detection and security. Here are just a few of the ways in which it can be utilized.
Underwriting and Risk Assessment
Generative AI can be used to automate the underwriting process. When it’s fed data about a customer’s age, occupation, health, driving history, and other risk factors, it can generate predictive models that allow insurers to calculate appropriate coverages and premiums.
For property insurance, it can also assess risks related to weather patterns, rising costs, and even climate change.
Automating the underwriting process can reduce operational costs and improve efficiency, giving insurers time to devote to other important processes.
Synthesize and Simplify Information
Generative AI can undertake the tedious task of combing through explanations of policies and other complex documents to create short, easy-to-understand summaries for customers. It can also synthesize information it draws from multiple sources. This is helpful for customers, who may have difficulty understanding complex jargon or simply don’t have the time to read everything.
These analytical capabilities of generative AI can also help out insurers by scanning news, blogs, and social media for trends and customer feedback. This enables insurers to learn more about the market in much less time.
In addition, AI’s writing capabilities can produce content such as staff training materials. It can also translate content between different languages, which is helpful for both staff and customers.
Tailored Marketing and Customer Service
The same types of analytical tools can be helpful for creating marketing content that is tailored to the needs of individual customers. Predictive analysis allows insurers to create different marketing campaigns that can then be targeted to different groups of customers.
Customer service can also be customized to individual needs through self-service channels like virtual assistants and online chatbots. If the AI tools are fed the information from the right documents, it can synthesize it and provide straightforward answers to questions from buyers.
Analyzing market trends through AI can also allow insurers to create and offer more innovative products and services.
Fraud Detection and Security
Generative AI could prove incredibly useful in claims processing. Because of its ability to detect anomalies, it can alert insurers when there is potential fraud in claims. It can also accelerate claims processing, saving operational costs and improving efficiencies.
In addition, blockchain and generative AI can enhance security in claims processing—however, there are also some security and privacy concerns with using AI to analyze customer data, so it is important to use it safely and ethically.
Challenges to Adopting Generative AI
First, it is crucial that your business’ use of AI complies with policy and regulations. This is challenging considering how these policies are rapidly changing as the technology develops into unprecedented territory. It may not always be clear right away if a certain AI tool complies with the law.
Second, even if it does comply with current legal regulations, it’s important to consider the ethics. The laws surrounding AI can’t always keep up with data privacy concerns. Businesses must ensure that they can protect the privacy of their customers while using AI, and they should always obtain consent from customers to use their data in predictive analysis tools.
AI tools also can experience what’s called training bias. These tools learn from being “fed” data. If the data they are fed is not from diverse datasets—or if these sources and datasets hold biases, whether intentional or not—the AI can become discriminatory.
On this note, another challenge is that training AI requires high-quality data—and a lot of it. Many insurers may not have access to this amount or quality of datasets to feed the AI. Building the AI tool to its fullest capacity will also take time and significant supervision—it’s just like hiring a new employee. To ensure the training is done properly, insurers may need to employ a team of IT specialists, data scientists, and other experts.
Lastly, there is value in real human-to-human interactions, and in this realm, AI is obviously lacking. Customers may feel a lack of empathy when communicating with a virtual assistant or chatbot in comparison to a real person. This is important to consider when switching to generative AI tools.
There are a lot of AI tools and solutions being announced and marketed right now, and that trend is likely to continue throughout 2024. It will take a lot of research and sifting to find the right tools for your company. Likely, it will be best practice to combine multiple AI technologies when automating your business practices, instead of relying on just one. However, concerns of privacy, bias, empathy, and cost effectiveness must first be addressed.
