The Ways and Benefits of Using Chatbots and Virtual Assistants in the Insurance Industry

Main Thoughts

• AI virtual assistants and chatbots can facilitate FNOL and claim submission.
• AI chatbots can help stave off fraud with the help of Voice Recognition.
• It is possible to use AI chatbots to initiate various workflows and perform various tasks.
• AI chatbots can be used in ensuring customer compliance.

Chatbots and virtual assistants are computer programs that use Artificial Intelligence (AI) and Natural Language Processing (NLP) to emulate human speech, converse with customers, answer their questions, and guide them through certain actions and procedures. They help provide 24/7 customer support to a large number of concurrent customers, while dramatically relieving the workload on human customer support employees.

Insurance is one of the industries where service providers’ interactions with their customers can be extremely information-intensive. While customer service must quickly answer customer inquiries by managing significant amounts of diverse information, customers often have to figure out rather complex info on the fly. In this information-intensive environment, chatbots, virtual assistants, and AI robo advisers have emerged as powerful aids that are often difficult to do without.

In this Guide, we will dwell on the benefits developing a chatbot or virtual assistant can bring to an insurance company.

Improved Customer Experience

Among the many benefits of chatbots, one definitely stands out and accounts for the bulk of their adoption: chatbots are capable of tirelessly serving customers round the clock. They don’t need to break for lunch or rest. Chatbot’s constant availability makes it much easier to cater to the needs of customers in different geographies and time zones without making most of one’s customer support employees work inconvenient shifts.

Provided they are properly scaled to your needs, chatbots are available not only constantly, but also instantly. Keeping a caller on the line can be annoying. Chatbots can pick up calls at once, which promotes customer engagement and can even influence customers’ buying decisions.

Since advanced chatbots сan manage vast amounts of information from diverse internal (databases, correspondence, CRM, other) and external (the whole of the Web) sources, they have more context while talking to a prospect or client. They cannot simply forget or omit something of importance and can mention something of essence just when this is necessary. This allows chatbots to tailor the recommendations they deliver and try to max out the effect just when this makes sense.

Enhanced Efficiency in Claims Processing

Claim Management is one of the main insurance business processes. Chatbots can facilitate claim processing and make it a great deal more reliable. Furthermore, they can be extremely helpful throughout the entire lifecycle of a claim.

As claims are often filed in stressful circumstances, any delays in responding to customers’ calls can be extremely frustrating to them. Chatbots can answer without delays and help file a First Notice of Loss (FNOL) in any circumstances. They can guide the customer through both submitting a FNOL and creating a formal claim on mobile, while sparing them the lengthy completion of the required forms. A chatbot can also fill in part of the form and check which of the documents the claim is to be accompanied with are already available to the insurance company.

Processing submitted claims becomes faster, less time-consuming, and more reliable. An AI insurance chatbot that is coupled with an AI model can determine whether any documents attached to a claim are authentic by using past data. This way an AI bot can determine whether the claim is valid or not.

Notably, AI chatbots can do the same with images too. For example, they can process an image of a dented car and collate the information they’ve extracted with the description of the damages in the claim. Similarly, it is possible to use AI chatbots for property damage assessment purposes. Also, a chatbot can classify claims and process them in accordance with different predefined rules to speed up the procedure.

Settling claims becomes faster and more hassle-free too: once a claim is recognized as valid, a chatbot can issue instructions to make a payment in order to settle it. Customers can use AI insurance chatbots to add information to their claims, or find out about the current status of their filed claims. Besides, chatbots can proactively approach customers to elicit some missing information, or request them to take some action in relation to their claim. They can also generate and send reminders for this purpose.

Finally, AI chatbots can help detect and prevent fraud in claim processing. To do this, they can compare in real time the voice of a claimant with their voice imprint, taken by the insurer during the KYC procedure.

Cost Reduction and Operational Efficiency

An insurance company’s resource bandwidth is to a significant extent eaten up by repetitive tasks and the same questions, asked by different customers. Because of this, AI chatbots can reduce the customer service workload very significantly. Correspondingly, one doesn’t need to employ a larger number of customer service representatives, train, and insure them. It is generally believed that chatbots can trim the customer service costs of various companies, and in particular, those of insurance companies by around 30%.

Additionally, chatbots free up customer agents’ time. They allow them to spend more time on more complex tasks and thus achieve a higher quality of their fulfillment.

While implementing a chatbot, sufficient attention should also be paid to its integration with the backend of one’s insurance solution. This will allow one to implement the automatic initiation of complex workflows, like, for instance, insurance policy modification, bill payment, the processing of low-risk, small-size claims initiated via the chatbot, and others. Such integration will also allow the chatbot to retrieve data from the insurer’s legacy systems and leverage this data.

To ensure a good integration ability of an insurance company’s systems, all of them must be limitlessly scalable. They must use standard protocols, formats, and interfaces, and include well-developed interoperability features.

Data Collection and Insights for Insurance Providers

AI insurance chatbots can gather all kinds of customer data and immediately use it to offer personalized advice and guidance to the customer they are having a conversation with. The collected information can include the customer’s financial standing, assets, car model, health condition, and other factors. Chatbots can take all or some of these factors into consideration to offer tailored insurance plans, products, packages, and quotes using the kind of terminology that should be more understandable to the target audience the customer belongs to.

Simultaneously, insurance chatbots collect for future use data on the customer’s interests, preferences, apprehensions, wants, and needs that has become available during the conversation.

When coupled with an AI recommender model, AI chatbots can be extremely useful when it comes to underwriting. They can collect data from a variety of sources, including public records, social media, credit reports, and others. By analyzing this data, chatbots identify and assess any customer risks that exist. Taking these risks into consideration, they calculate the more suitable policy premiums. This way, AI chatbots enable insurers to achieve savings on policy premiums, trim their underwriting costs, and speed up the underwriting process.

Seamless Integration with Other Channels

More than 80% of customers interact with their insurers through different mediums. These mediums include insurer’s websites, mobile devices, social media, and various messengers. This makes the ability to provide equally seamless and appealing customer experience across all these channels. Fortunately, AI insurance chatbots can become part of your omnichannel experience across the board. This means not only insurance websites and mobile apps, but also various messaging apps, like WhatsApp and FB, and smart home devices.

Whenever the capacity of a chatbot falls short and it has difficulty in answering customer questions, the chatbot can seamlessly hand the conversation over to a human customer service representative. Furthermore, the chatbot can then summarize the previous part of the conversation and convey its gist to the customer service representative, so that they can seamlessly pick up from where the chatbot left.

Improved Employee Productivity and Satisfaction

In addition to improving an insurance company’s interactions with their customers, insurance chatbots and virtual assistants can also improve some of these companies’ internal operations. This includes workflows, the interactions between different platforms, and the collaboration between different teams of employees that are based in different locations. Internal AI chatbots make company-wide messaging a great deal easier and cost-effective by allowing constant and instant access to the information employees need to fulfill their tasks.

Besides, it is possible to save an insurance company’s IT employees’ time and cut the related costs by developing an IT help desk chatbot.

AI chatbots can use voice input to launch various workflows, for example, the running of scans, or creation of backups. When a workflow is complete, a chatbot can provide feedback on some related details, or just inform the employee in charge about the completion of the workflow.

Similarly, based on conversational inputs, voice-guided virtual assistants can initiate the assignment of tasks, create tickets, or update the statuses of some other tasks they’ve been involved in performing. They free up employees’ time and enable employees to focus on some other, higher-value activities.

Lastly, AI chatbots can be a great help to one’s insurance agents, who need to remember a lot of information and keep tabs on a diverse range of factors, like the conditions of the different insurance policies, premiums, coverages, term limits, renewals, promotions, discounts, and more.

Addressing Compliance and Regulatory Requirements

Insurance AI chatbots can promote customers’ compliance by answering their questions about compliance topics, or raising a flag when a customer wants to take an noncompliant action during a conversation with them. They can use Machine Learning to analyze the entirety of their interactions with customers (including text, voice communications, and videos) to ensure customer compliance.

As it is a full-value part of an insurance company’s operation, any AI insurance chatbot must be fully in sync with the compliance requirements.

Unfortunately, using generative AI for conversational purposes can be a security hazard if proper data security safeguards are not put in place. Incidentally, the issue of data leakage through AI chatbots has already been broached and addressed by several leading U.S. and European companies and banking institutions, which just once again goes to show it should not be taken lightly.

First off, insurers’ should separate and isolate any internal confidential data that they do not want to be made available to their customers or publicly in order to prevent it from being leaked by their AI chatbots. The conversational logic of an AI chatbot must be designed in such a way that it will support only approved conversational directions, thus preventing the chatbot from leaking any personal, transactional, or other confidential information.

To ensure this, one must develop an AI chatbot’s conversational logic in a compliant manner. The logic validation procedure must necessarily involve qualified human experts and be conducted every time before a new version of this functionality is released. To rule out any possible gaps, it is also necessary to employ specialized software to test all the possible conversational scenarios. The data the chatbot will have access to must in its own right be in strict compliance – with either GDPR, or some other regulations applicable in the corresponding geography.

Additionally, it is also necessary to protect a chatbot’s communication channels. One can do this with the help of strong encryption algorithms (like AES, Twofish, Blowfish, or RSA) and some reliable user authentication technology (Identity Verification with Biometrics or advanced Face Recognition).

Future Potential and Innovations

As the rise of AI and NLP continues, the understanding ability of AI chatbots will grow, which bodes well for their future in all industries, including Insurance. However, the trend that will unfold big time is the integration of insurance chatbots with IoT and wearables.

Cognitive chatbots will likely use IoT and telematics data at scale to enhance the underwriting and claim-processing in car insurance, home insurance, and medical insurance. Simultaneously, it will become much easier for insurance companies’ clients to keep various risks in check by issuing voice instructions to bots that will convey these instructions to IoT devices. This will reduce insurance provider’s premiums-related expenses in some of the insurance niches.

The capabilities of virtual assistants will grow with the further rise of AI too. More of them will use emotional intelligence and learn to leverage the emotional context of their interactions with the customers. They will become more proactive and will render context-aware assistance.

AI virtual assistants will also learn to collaborate on tasks with humans, interacting with them for this purpose more coherently and efficiently.

Conclusion

AI chatbots and virtual assistants are bound to significantly impact the insurance landscape in the coming years. They have the potential to impact all the major insurance business processes, further expand the opportunities created by IoT, Telematics, and Wearables, and help ensure both insurer and customer compliance.

Because of this, those insurance businesses that want to begin their digital transformation should make implementing an AI chatbot one of their major priorities as far as AI/ML functionality is concerned.