Chatbots in Insurance: Use Cases

The insurance industry is mostly identified with heavy paperwork, complexities, and legacy processes. Today, the offers and products provided by an insurance company are not enough to set it apart. Customer experience is the only brand differentiator that should not be ignored and support centers are the solution to meet the changing customer requirements. However, such customer support centers are expensive as well as obsolete because the customers find it difficult to access the support assistants through calls and messages. In addition, large customer service is required for a large number of customer requests, ensuring 24/7, individual interaction across multiple digital channels and different languages. AI chatbots are the best solution to meet these standards of customer experience. 

Use Cases of Chatbots in Insurance 

Here are some of the major use cases of chatbots in the insurance sector: 

Customer support and awareness  

Based on the profile and inputs of the customer, the customer service chatbots can create awareness among the customers about the working of the process in multiple carriers, compare, and suggest optimal policy. In order to conduct end-to-end processes without interventions, insurance companies should be ready to assist customers in an interactive and secure manner. Customer service chatbots increase conversions by interacting with all visitors and engaging them on your website or any other channel. 

Lead profiling and conversions  

Research shows that if a response is not given to a customer’s question within 5 minutes, the chances of it becoming a lead are reduced by 400%. Such situations can be avoided with the presence of an insurance chatbot as it not only increases the lead conversion but also makes the user happy with an immediate response. Moreover, chatbots can provide relevant details to the customers depending on the input and queries they give. This data will help the sales team as it offers a complete context of what an active user is looking for and move forward accordingly. 

Claim processing and payment assistance  

Chatbots are designed and programmed to resolve issues related to the insurance claims of your customers and to track the existing policies. This will not only encourage the customers to make their upcoming payments but also assist them in making the payment through simple and easy steps across the channel preferred by the customer. 

Rich database  

A long and growing e-mail contact list is important to every business, and insurance companies are no different from that. Almost all chatbots are built to gather the contact details of the customers with whom the bot interacts. Further, the contact details collected by the chatbot are added to the user database for communication through various mediums. 

Reduce workload  

Automation mainly aims at reducing the workload, indeed most technological advancements serve the same purpose. With chatbots being installed in the insurance sector, there is a significant reduction in the workload on the sales and marketing team as they avoid the hassle of having to answer each question individually and concentrate on turning the leads into sales.  

Pre-sales and sales  

According to the studies, almost 53% of customers prefer online purchases if they are given the option to converse with the dealer directly. An insurance chatbot allows you to establish a strong relationship between the customer and the brand. At the same time, it identifies and distinguishes the customers on the basis of their purchase intent.  

Personalized customer experience  

It is obvious that customers like to engage in real-time interaction rather than emailing. The reason is that people often identify websites as static mediums, so any kind of interaction that takes place in the media provides a better customer experience. Providing excellent deals and advice on insurance claims and quota is the actual merit of obtaining customer statistics. Chatbots can also make an appropriate recommendation by monitoring the behavioral patterns and habits of customers. Additionally, it prompts customers to leave positive reviews and gather their feedback. 

Key Features of insurance bot 

Many organizations might have installed chatbots but not all of them may not meet the specific standards a chatbot should have. Here are some of the key features of a chatbot: 


We expect an insurance advisor to give proper assistance in account opening, providing information about the policies and quotas, signing the claims, etc. A virtual assistant is supposed to perform the same functions as an advisor, which can be done by back-office integration of company systems. Such intricate integration cannot be performed by every technology in the market.  


Virtual assistants should be able to comprehend customer queries in a way a human support assistant would do. They must give a proper response after analyzing the context of the message. Whenever the customer reports a query or an issue, there should not be a reason why a chatbot is unable to comprehend it immediately. 


Any misinterpretations or transaction errors can lead to loss of customers and negative reviews. The customers should be able to recognize their insurance company as reliable. Providing a seamless customer experience is one way to gain the trust of the customers and to a large extent, conversational AI chatbots serve the purpose. 


It is always better to deploy your chatbots in multiple channels. This gives the customers the opportunity to choose any channel of their preference and convenience – which can be a phone call, WhatsApp message, social media platform, or even a mobile app.  

Wrap Up 

The ultimate goal of any insurance chatbot is to provide a personalized, native, and interactive experience to the customers. Chatbots can minimize functional costs, increase revenue, promote brand engagement, and improve customer loyalty. However, this is possible only if they naturally interact with customers in a way they prefer. As a result, opting for the right development platform is critical to the success of chatbots in the insurance sector as it has to provide reliable and measurable optimal conversational features. 

Difference Between Chatbots and Conversational AI

Today personal and professional interactions are becoming more and more digitized. Such digital environments are essential for business-to-customer relationships to nurture. Technology has become more advanced and is getting advanced day by day, thus increasing effective communication between customers and computers. The customer-computer relationships are mostly backed by chatbots and conversational Artificial Intelligence. In this blog, let us talk about conversational AI and chatbots and delve deeper into the relationship between the two. 

What is a chatbot? 

A chatbot is recognized as a digital agent that uses simple technologies to initiate communication with customers through a digital interface. Chatbots are automated to ‘chat’ with customers through websites, social media platforms, mobile applications, etc. They are not complicated to build and do not require technical proficiency. Chatbots can be easily built with both development platforms and can be implemented on digital channels.

What is conversational Artificial Intelligence (AI)? 

Unlike chatbots, conversational AI is not about rule-based interventions, they have a firm structure and mostly relies on ML (Machine Learning), NLP (Natural Language Processing), Input Analysis, and DM (Dialog Management) to offer a more dynamic and unrestricted user experience than chatbots. Conversational AI uses a dialog flow system to provide a more advanced and exceptional service experience in human-bot interaction. Simply put, it refers to a set of artificial intelligence technologies that facilitates’ intelligent’ communication between computers and humans.  

Natural Language Processing (NLP)  

Conversational AI generates responses using linguistic rules and by incorporating machine learning and contextual awareness. Artificial Intelligence can customize the responses given to customers and predict their needs rather than simply interpreting the request of a user. NLP also enables machines to understand and comprehend voice as well as text inputs. Meanwhile, on the other hand, chatbots depend mostly on algorithms and language rules to interpret the meaning of a question and to select a proper response using natural language processing. 

Machine Learning (ML)  

Machine learning enables machines to converse intelligently with the users and to learn and understand from conversations. In Conversation ML, Systems with conversational ML enable machines to use their conversations with users to make future conversation experiences better.  

Dialog Management (DM)  

DM’s mission is to initiate conversations with customers and help them satisfy their needs. It ensures that the necessary semantic representation has been filled and determines the performance of the system. DM reaches out to the Knowledge Database in order to find the exact information the user is searching for. Dialog Management involves the selection of policies and tracking of the dialog state, thus enabling the dialog agent to make tough and powerful decisions.  

Input Analysis  

It helps to evaluate the purpose of the input and then generates a response that matches the context of the situation, which is exactly what a human agent would do while handling a customer query.   Input Analysis allows the machine to provide better recommendations and suggestions after analyzing the input information. 

Conversational AI V/S Chatbots 

We have discussed what Conversational AI and chatbots stand for. Now let us see what is the relation between the two.  

Have you ever thought about what makes a chatbot converse like a human? Well, the credit goes to Conversational AI. Conversational AI, when implemented in chatbots, makes them smarter and more efficient. But the important fact to be noted is that not every chatbot has conversational AI induced in it. There are these traditional chatbots that can perform only a limited number of tasks, which usually involve responding to common FAQs. The increasing needs of large enterprises led to the implementation of conversational AI into chatbots, thus improving their functionality from merely being an answering bot to understanding human language and providing transactional functionality. Conversation AI enables you to perform much more things efficiently rather than translating web content into chatbot responses. 

Differences between AI-driven chatbots and Traditional Chatbots 

AI Chatbot – relies on Natural Language Processing, Machine Learning, and Input Analysis to give a personalized customer service experience. 

Traditional Chatbots – rely on rule-based functioning or programmed conversational flow. 

AI Chatbot – strong and non-linear interactions that go all the way to deliver an appropriate response to customers. 

Traditional Chatbots – linear and pre-set interactions that do not go out of the scope.

AI Chatbot – learn and understand continuously from interactions and rapid repetition cycle. Adapts to the changing customer needs. 

Traditional Chatbots – interpret the input information. The reconfiguration will be necessary to update or revise any pre-defined rule and conversation flow. 

AI Chatbot – scalability is high. The conversational AI interface gets updated while updating the database and pages of the company. 

Traditional Chatbots – take a lot of time to scale. Requires updations, maintenance, and revisions to be done manually. 

AI Chatbot – handles a large amount of data from clients at a faster pace. Gives rapid solutions to problems. 

Traditional Chatbots – rapid response but fails to respond to questions out of scope.  

AI Chatbot – Dialog-focused. 

Traditional Chatbots – Navigation-focused. 

Why do Conversational AI chatbots outrun Traditional Chatbots? 

Looking at the above-mentioned points, one can easily understand that conversational AI is way more interactive and has many advantages than a chatbot. Now comes the question ‘Why chatbots are still in the market?’. The answer lies in the specific needs of organizations with different sectors, sizes, and business models. For instance, let’s assume that you are a restaurant owner and you decided to implement a chatbot on your website. This way your users can easily order food, track the order and give feedback without even talking to the owner or any other representatives. The chatbot will deliver proper service as long as the user remains in the scope topic. Chatbots are enough for small and medium businesses and huge companies which aim to handle a single task. 

Wrap Up 

Most people deem that these two terminologies are supportive and complementary to each other. They can improve customer interaction and experience when these two terminologies are effectively integrated. While comparing chatbots and conversational AI, you will see what makes conversational AI chatbots the best choice for your business. The system takes time to set up and train but once set up, a conversational AI is basically superior at performing most tasks. Therefore, it is highly recommended for businesses to gain better customer satisfaction.