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Top 10 Insurance Chatbots Applications & Use Cases in 2024

Top 10 Insurance Chatbots Applications & Use Cases in 2024Top 10 Insurance Chatbots Applications & Use Cases in 2024

Today around 85% of insurance companies engage with their insurance providers on  various digital channels. To scale engagement automation of customer conversations with chatbots is critical for insurance firms.

The problem is that many insurers are unaware of the potential of insurance chatbots. This article will introduce the top 10 use cases for insurance chatbots, including cross-selling, claims processing, damage assessment, and more, in order to close the knowledge gap and show the benefits of these tools for the operations of insurers.

Top 10 chatbot use cases in insurance

Chatbots enable 24/7 customer service, facilitate ordinary and repetitive tasks, as well as offer multiple messaging platforms for communication.


Salesforce is the CRM market leader and Salesforce Contact Genie enables multi-channel live chat supported by AI-driven assistants. Salesforce Contact Center enables workflow automation for many branches of the CRM and especially for the customer service operations by leveraging chatbot and conversational AI technologies for a personalized customer experience.

Insurance providers can leverage chatbots in:

Insurance policies

When a chatbot holds a conversation with policyholders or potential customers, it can:

1. Provide customized quotes to new customer

Chatbots can collect information about the customer’s finances, properties, vehicles, health status, and relevant data to provide advice on quotas and insurance claims, and offer potential customers an overview about the available insurance solutions that fit their criteria.

2. Cross-sell

Chatbots can leverage recommendation systems which leverage machine learning to predict which insurance policies the customer is more likely to buy. Based on the collected data and insights about the customer, the chatbot can create cross-selling opportunities through the conversation and offer customer’s relevant solutions.

3. Answer policyholder queries

Chatbots can provide policyholders with 24/7, instant information about what their policy covers, countries or states of coverage, deductibles, and premiums.

Claims management

Claims vary based on insurance types and insurance providers. However, all claims pass through the following steps:

4. Claims filing

Claim filing or First Notice of Loss (FNOL) requires the policyholder to fill a form and attach documents. A chatbot can collect the data through a conversation with the policyholder and ask them for the required documents in order to facilitate the filing process of a claim.

5. Damage assessment

Based on the insurance type and the insured property/entity, a physical and eligibility verification is required. A chatbot can ask the policyholder to send pictures or videos of property damage (e.g. a car accident) to inspect the damage, send it to a human agent or use AI/ML image recognition methods to verify the damage and determine liabilities depending on the context.

6. Claims processing

After the damage assessment and evaluation is complete, the chatbot can inform the policyholder of the reimbursement amount which the insurance company will transfer to the appropriate stakeholders.

To discover more about claims processing automation, see our article on the Top 3 Insurance Claims Processing Automation Technologies.

7. Settlement

At this stage, the insurance company pays the insurance amount to the policyholder. The chatbot can send the client proactive information about account updates, and payment amounts and dates.

For example, Metromile, an American car insurance company, used a chatbot called AVA to process and verify claims. AVA was able to approve 70-80% of claims immediately.

8. Fraud detection

Fraudulent activities have a substantial impact on an insurance company’s financial situation which cost over 80 billion dollars annually in the U.S. alone. AI-enabled chatbots can review claims, verify policy details and pass it through a fraud detection algorithm before sending payment instructions to the bank to proceed with the claim settlement.

Broker management

Brokers are institutions that sell insurance policies on behalf of one or multiple insurance companies.

9. Q&A

Though brokers are knowledgeable on the insurance solutions that they work with, they will sometimes face complex client inquiries, or time-consuming general questions. They can rely on chatbots to resolve those in a timely manner and help reduce their workload.

10. Communicating new policies

When a new customer signs a policy at a broker, that broker needs to ensure that the insurer immediately (or on the next day) starts the coverage. Failing to do this would lead to problems if the policyholder has an accident right after signing the policy.

Most of the communication of new policies between the broker and the insurance company takes place via structured data (e.g. XML) interchanges. However, some brokers have not embraced this change and still communicate their new policies via image files. Insurers can automatically process these files via document automation solutions and proactively inform brokers about any issues in the submitted data via chatbots.

Insurance companies can also use intelligent automation tools, which combines RPA with AI technologies such as OCR and chatbots for end-to-end process automation.

Future of chatbot implementation in insurance

According to Mckinsey, Covid-19 pandemic enhanced the customer preference towards non-physical conversations in general, and the insurance sector is not an exemption though, some of the insurance customers prefer human contact. Thus, customer expectations are apparently in favor of chatbots for insurance customers.

In addition, AI will be the area that insurers will decide to increase the amount of investment the most, with 74% of executives considering investing more in 2022 (see Figure 2). Therefore, we expect to see more implementation opportunities of chatbots in the insurance industry which are AI driven tools.

Figure 2: Technologies that insurers invested in 2022.

Image shows insurers investing in AI heavily.
Source: Deloitte

For a better perspective on the future of conversational AI feel free to read our article titled Top 5 Expectations Concerning the Future of Conversational AI.

For more on chatbots

If you are interested in understanding the underlying technology behind conversational AI and chatbots, read our articles on voice recognition applications and natural language processing

And if you want to read about use cases of chatbots in other industries, feel free to read these articles about chatbots in:

If you are ready to implement conversational AI and chatbots in your business, you can identify the top vendors using our data-rich vendor list on voice AI or conversational AI platforms.

To learn more about how natural language processing (NLP) is useful for insurers you can read our NLP insurance article.

And we can help you as well:

Find the Right Vendors

Source: Figure 1.

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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