How AI is transforming the insurance industry [Infographic]

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Susan Meyer

Senior Editorial Manager

  • Licensed Insurance Agent — Property and Casualty

Susan is a licensed insurance agent and has worked as a writer and editor for over 10 years across a number of industries. She has worked at The Zebr…

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Beth Swanson

SEO Content Strategist

  • Licensed Insurance Agent — Property and Casualty

Beth joined The Zebra in 2022 as an Associate Content Strategist. She is a licensed insurance agent whose goal is to make insurance content easy to r…

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AI and insurance

It’s hard to find an article today that isn’t concerned with the AI revolution. Will it come for our jobs? Is it reliable? How is it impacting [blank] industry? Is this article you’re reading written by AI? (Spoiler: it is not; I am a person. But that’s probably also what a well-programmed chatbot would say.)

In many ways, the insurance industry is a perfect candidate for leveraging AI technologies. After all, it’s all about using tons of data to make estimations about what might happen. And what’s better at that than a computer?

As insurance companies become more digitized, traditional companies and smaller startups alike are searching for ways to improve their businesses.

AI can help insurance companies make sense of large amounts of data, streamline operations, help with fraud detection and provide insights to offer better products. Jump to the infographic for some highlights on how insurance companies are using AI in action or keep reading to learn more. 

What is AI?

Artificial intelligence replaces tasks that normally require human intelligence with computerized systems. Two more concepts emerged as these technologies advanced: machine learning and deep learning

  • Machine learning is a subset of AI in which computers are trained to analyze data sets to find patterns or predict outcomes. They improve as they’re exposed to more data sets over time. 

  • Deep learning is a subset of machine learning that can analyze vast data sets and train itself to perform a task. Unlike machine learning, deep learning technologies do not need to be externally trained.

As a whole, these technologies have given companies a better understanding of consumers’ needs by analyzing data. When combined with other tools like computer vision and natural language processing, AI solutions can provide faster, smarter and customized products. 

Other types of technologies behind AI include:

  • Computer vision is how computers analyze and make sense of digital images. One example is Facebook’s ability to recognize faces in photos and predict users’ identities.

  • Natural language processing is how computers understand human languages and speech. Siri, chatbots and predictive text can all utilize natural language processing. More recent developments like ChatGPT, built on open AI, are trained to follow instructions and provide a detailed response.

  • Automated speech recognition is how computers analyze sounds to turn them into words. Speech-to-text systems are one example. 

Ways AI can impact the insurance sector

AI has the potential to impact all areas of the insurance industry. Investing in and integrating AI solutions is critical to maximizing business efficiency, reducing risk, and improving the customer experience. 

As such, it's not surprisingly a growing market. One research group found that the AI in the insurance market was valued at $3.64 billion in 2022 and was expected to reach $35 billion by 2030.[1]



Risk assessment

Traditionally, insurance underwriters have assessed the risk posed by an applicant through applicant-provided data. They can then make estimations of the degree of risk the applicant poses to their insurance provider and can decide whether or not to accept them, and also what rate will be charged.

AI tools can help insurers by analyzing big data sets to gain deeper and more accurate insights into customer behavior and provide better risk management for the insurance company. AI can help identify potential disparities in decision-making based on race, gender or ethnicity, making the underwriting process more fair and uniform.

Fraud prevention

The FBI reports the cost of non-health insurance fraud totals nearly $40 billion per year, so you can bet that companies take fraudulent claims seriously.[2] AI-powered solutions can spot unusual patterns in historical data and comb through massive amounts of information to identify potential fraud. These new technologies can improve over time with advanced algorithms, evolving to identify new threats. 

There are several use cases for insurtech in fraud prevention. One example is in catching fraudulent claims. Predictive analytics can analyze factors such as the frequency of claims made by the person, time between claims, type of claims submitted, etc. and even track them across different insurance providers.

Faster claims processing

As it stands, the claims process is very manual and time-consuming. Chatbots and other natural language processing technologies can cut down on some of the administrative tasks, and provide real-time assistance with insurance claims, possibly with more accurate results too. Smartphone cameras are another AI tool that can assess damages to help speed up one of the more tedious of insurance processes

Customized solutions

Consumers now have the ability to build insurance policies to fit their needs rather than selecting a one-size-fits-all policy. Usage-based car insurance plans are one example of how companies are tailoring their products to give users what they need.

In these plans, drivers use telematics devices (either through an app on their phone or a device) to share data on their driving behavior and tendencies with insurance companies. In return, this allows the insurance business to more accurately price their premium

Operational optimization

All businesses aim to cut down on unnecessary workloads to make them more efficient. That’s why many companies have partnerships with chatbots and natural language processing tools to field customer requests online and over the phone to reduce administrative tasks.

The use of AI can streamline internal operations, allowing workers and time to be allocated to more valuable tasks. 

Improved customer experience

Users want reliable, easy-to-access service. Chatbots and smartphone apps are two common examples of how companies are making themselves more accessible through automation and connectivity.

With these technologies, users can log on at any time of day from any location to get in touch with an insurance agent or start the claims process. In return, these tools help reduce costs for the company and lead to faster processing. 

The future of insurance and other financial services is likely to continue using AI models to become more efficient and improve profitability.