How AI is transforming the insurance industry [Infographic]

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, is trained to follow an instruction 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. 

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.

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, 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 workload 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 has the ability to 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 are likely to continue using AI models to become more efficient and improve profitability.