Top 10 AI Use Cases & Applications Insurers Must Know in 2024

According to Deloitte, in order to enhance their operational efficiency, insurers spended the most on AI technology. In 2022, for instance, 74% of insurance executives planned to increase their investment in AI (see Figure 2).

Many insurance operations, such as:

• Claims processing 
• Appeals processing, 
• Personalized insurance pricing, 
• And fraud detection, 

Can be automated by AI technologies such as OCR,  document processing, chatbots, and affective computing. The applicability and flexibility of AI models is the reason why insurance executives are interested in them. 

In this article, we demonstrate the top 10 AI use cases of AI insurance. 

Figure 2:  Insurers’ decisions about their spending on technology investments.

1. Application processing including insurance underwriting

Application processing requires extracting information from a high volume of documents. Performing this task manually is time-consuming and error-prone.

Document capture technologies enable insurance companies to automatically extract relevant data from application documents and accelerate insurance application processes with fewer errors and improved customer satisfaction.

For more, feel free to check our article on AI in underwriting.

Claims processing

Claims processing includes multiple tasks, including review, investigation, adjustment, remittance, or denial. While performing these tasks, numerous issues might occur:

• Manual/inconsistent processing: Many claims processing tasks require human interaction that is prone to errors.
• Varying data formats: Customers send data in different formats to make claims.
• Changing regulation: Businesses need to accord in changing regulations promptly. Thus, constant staff training and process updates are required for these companies.

2. Claims document processing

Claims processing includes multiple tasks, including review, investigation, adjustment, remittance, or denial. While performing these tasks, numerous issues might occur:

• Manual/inconsistent processing: Many claims processing tasks require human interaction that is prone to errors.
• Varying data formats: Customers send data in different formats to make claims.
• Changing regulation: Businesses need to apply new regulations to their processes as soon as they are introduced. Thus, insurance companies must train their staff and update their processes constantly. 

Insurers can use NLP-driven technologies and document capture to  handle large volumes of documents. This can help companies process documents rapidly, save time and costs, detect fraudulent claims, and check if claims fit regulations.

You can also check our lists of AI tools and services:

• Data Science / ML / AI Platform
• Insurance Suites
• AI Consultant
• AI/ML Development Services

You can read our article on the Top 3 Insurance Claims Processing Automation Technologies to learn more about claims processing automation.

3. Automated repair cost estimation

Insurtech companies can deploy AI models to perform repair cost estimation:

• AI systems can receive accident images,
• Analyze them,
• Compare them to previous images they’d received for other cases, 
• And provide an estimated repair cost accordingly in real time.  

For instance, Bdeo, an insutech business, uses computer vision models to enhance the claim adjustment process. As a result, insurers increase the efficiency of their claims processing and the accuracy of their estimations. 

4. Insurance pricing

According to the Word Economic Forum, 42 billion IoT-connected devices such as cars, fitness trackers, home assistants, smartphones, and smartwatches are expected to be used by 2025 globally. 

As these devices become more popular, the amount of consumer data rapidly increases.

IoT devices allow insurance companies to use the wearers’ real-time vitals data to use alongside other data, such as: 

• Lab testing data, 
• Biometric data, 
• Claims data, 
• And health data. 

When evaluating their customers’ risk profile. 

Ideally, this will result in more accurate insurance pricing, meaning less risky customers enjoying lower premiums and vice versa. This increases equitability, insurers’ profitability, and their market outreach].

You can also learn more about business insurance pricing. 

5. Document creation

Insurance companies need to generate high volumes of documents, including specific information about the insurer. 

While creating these documents manually is time-consuming and error-prone, using AI and automation technologies such as RPA can generate policy statements based on rules-based criteria, which minimizes mistakes, increases compliance, and ensures accuracy.

Learn more about document automation. 

6. Responding to customer queries

Conversational AI technologies such as chatbots can play a critical role while interacting with customers. As responding to customer queries can be a tiresome task, simple queries can be handled by chatbots, which enables employees to focus on higher value-adding activities.

7. Understanding customers better 

Insurance companies can benefit from affective computing, also known as emotion AI, to understand customers better and take action according to their mental states. 

For instance, affective computing can pick up on the callers’ voice tone, volume levels, and enunciations to assess their level of anger, hopelessness, agony, etc. to intelligently route their calls to more experienced call agents to ensure their satisfactions and to address their needs as best as possible.  

8. Insurance fraud detection

About 30% of insureds have admitted to lying to their car insurance company to gain coverage in the US. Text analysis and AI-powered predictive analytics might detect fraudulent claims based on comparing the data captured from the claimant’s story with the insurance’s business rules.  

Insurance companies can also benefit from voice analytics to understand if a customer is lying while submitting a claim.

Learn more about the technologies powering insurance fraud detection. 

9. Personalized services

According to an Accenture study, 80% of insurance customers want more personalized experiences and are willing to disclose their personal data in exchange. 

By using AI, insurance companies can better understand their customers and offer customized products that enable individuals to only pay for the coverage they need. For instance, insurance companies can offer a customized policy interpreting the applicants’  driving data, such as their speeding tickets, the number of times they’ve been pulled over, the number of accidents, and more.

Data-driven services can increase  the appeal of insurance to a wider range of customers, especially considering that in the US, 9.2% of people have no health insurance and may be purchasing a policy for the first time. 

10. Appeals processing

After the claims are processed, some can face  appeals, which is automatable via a combination of AI, OCR, and RPA.  

Thus, insurers can reduce their expenditures while offering a faster appealing process for their customers.

Future of insurance with AI

AI will transform the insurance sector in the future with new advances in deep learning and big data analytics:

• Product innovations:
  • Advanced AI algorithms will analyze data to find risks faster and more accurately, so dynamic products like usage-based insurance products can be launched in numerous domains beyond just auto insurance.
  • Thanks to increased customer data, personalized services can increase. However, this increase may be limited by new data protection regulations which will restrict collection, sharing and processing of personal information. These limitations will create a reduction in the flexibility of underwriting processes.
• Increased operational automation: Most claim processing tasks will be automated. The tasks of insurance agents will change with AI-enabled bots, smart devices, blockchain and advanced data analytic tools. They will be able to identify and communicate with potential customers faster.
• Risk reduction: IoT and digital twins solutions and new smart devices will play a key role to prevent home and car accidents in advance by real time monitoring.

For more on AI & Insurance

You can  check out other AI applications in marketing, sales, customer service, healthcare, or analytics. 

You can also read our other articles about AI and insurance:

• AI in Business: Guide to Transforming Your Company
• Future of AI according to top AI experts
• Top 6 Digital Transformation Applications in Insurance

If you have more questions, do not hesitate to contact us:

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