chatbot insurance examples 12

2025-03-05 News

Chatbots for Insurance Progessive, Allstate, GEICO, and More Emerj Artificial Intelligence Research

Customer service chatbots are buggy and disliked by consumers Can AI make them better?

“As with everything we do in the AI space, we are focused on implementing the technology responsibly, and keeping Accenture and client data secure,” Jenn Francis, a spokesperson for Accenture, said when asked to confirm a ban on ChatGPT. “We are seeing serious momentum within Goldman Sachs and potential use cases are pouring in,” Gupta previously told Insider. “We are in the process of prioritizing which use cases we implement first. We’ve launched several proof of concepts.” As of February, sources with knowledge of the bank’s internal meetings told Bloomberg that emerging tech like ChatGPT must be reviewed before it can be applied to business communications.

Data interpretation through machine learning will be an important application in the coming years for identifying business opportunities in an evolving market. In the full article below, we’ll explore the AI applications of each insurance company individually. We will begin with State Farm, the #1 ranking insurance company based on the 2016 National Insurance Commissioners ranking. Kumba is an AI Analyst at Emerj, covering financial services and healthcare AI trends. She has performed research through the National Institutes of Health (NIH), is an honors graduate of Rensselaer Polytechnic Institute and a Master’s candidate in Biotechnology at Johns Hopkins University. Common questions Personetics Assist can answer have to do with financial advice and other services the customer may have signed up for with the client financial institution.

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This gives them the information they need to keep up with top players in their industry, direct their efforts toward high ROI AI projects, and avoid AI applications that lack any evidence of widespread adoption. “Currently, over 70% of orders taken by our Presto Voice solution require human agent intervention,” it said in the filing. “As we continue to improve our AI accuracy and further deploy Presto Voice across store locations, we believe that the percentage of orders that do not require any human agent intervention will reach 30% or better.” The defense company told the Wall Street Journal in March that it will not share its proprietary company data with third-parties until tools like AI are reviewed and deemed safe to use.

Insurtech refers to the use of technology to automate and enhance processes in order to cut costs and improve efficiency in the current insurance industry model. Insurtech is a rapidly growing sector within the broader financial technology (fintech) space. The insurtech (a portmanteau of “insurance” and “technology”) that powers the industry is at the center of that, as companies integrate technologies like generative AI to make insurance more accessible, efficient, and tailored to individual needs.

NLP & AI-powered chatbots for insurance

If drivers for a service demonstrate safer driving habits, insurers can then offer that service lower premiums. Devices can also be used to activate insurance coverage only when drivers are actually driving, cutting costs while insuring service workers who would otherwise have had to purchase their own policies. There is a consensus among industry experts (both from our own insurance AI secondary research, and according to a 2017 Accenture survey report) that AI is going to be a key driver in making insurance products “smarter” in the coming 2-3 years. In addition, Progressive’s chatbot is intended for use by employees as well as customers. Even though GEICO is a trusted company, their lack of available information or success stories with their chatbot Kate does not allow us to verify that their chatbot in fact uses machine learning, although it appears it might.

It offers easy and fast access to information, consistency of response, ease of use, and 24/7 access. To access the chatbot operations, a user must provide an ID or passport number, the OTP is sent to the user’s registered mobile number for verification, and all the requests are sent to the client’s email. IBM is creating generative AI-based solutions for various use cases, including virtual agents, conversational search, compliance and regulatory processes, claims investigation and application modernization. Below, we provide summaries of some of our current generative AI implementation initiatives. Microsoft seeded it with anonymized public data and some material pre-written by comedians, then set it loose to learn and evolve from its interactions on the social network. As an assistant for human providers, Insel says, LLM chatbots could greatly improve mental health services, particularly among marginalized, severely ill people.

AI can do these tasks faster — and more cost-effectively — than human employees by training models with historical data and using the models to automatically process new customers and claims. Progress Software offers a software called Kinvey Native Chat, which it claims can help insurance companies offer a chatbot for self-service transactions using natural language processing. We can infer the machine learning model involved in the software would need to be trained on thousands of a client company’s insurance claims to begin working effectively.

10 Noteworthy Organizations Leveraging the Power of Generative AI – Spiceworks News and Insights

10 Noteworthy Organizations Leveraging the Power of Generative AI.

Posted: Fri, 28 Apr 2023 07:00:00 GMT [source]

Chatbots are highly efficient in getting healthcare insurance claims approved promptly and with ease, giving a sense of consolation to insurance industry professionals. They suggest the most suitable insurance policies and speed up the claiming process, providing clients with a strong sense of security and comfort. Virtual assistants and AI-powered conversational chatbots have become more prominent with their presence across the spectrum. In the era of digital customer experience, customers expect fast and easy conversational exchanges. A customer service chatbot is a conversational commerce tool that provides customer care via text chat, voice commands or both.

Therefore, this approach applies to conversational chatbots (Gkinko and Elbanna, 2023) and in the realm of fintech (de Andrés-Sánchez et al., 2023; Firmansyah et al., 2023) and insurtech (Zarifis and Cheng, 2022) powered by AI. The main arguments for its significance center on the relevance of its cognitive and relational dimensions defined in Glikson and Woolley (2020). In our context, the cognitive dimension of trust is manifested in the perceived effectiveness of chatbot technology for implementing procedures linked with active policies. Relational trust is identified as the confidence that policyholders have in the insurer’s implementation of chatbots, with the intention of enhancing their ability to provide satisfactory service (Zarifis and Cheng, 2022).

Presto Automations, a drive-thru automation technology provider adopted by fast food chainsin the US like Del Taco, Hardee’s and Carl’s Jr., previously claimed that over 95% of orders received by its chatbots were taken without staff intervention. The company even announced a collaboration with OpenAI in March 2023 to utilize ChatGPT to enhance its drive-thru voice assistant features for more natural and human-like interactions. Adoption of chatbots—coded programs that can engage in some degree of conversation with human inputs, often through the help of artificial intelligence (AI) or machine learning—is undoubtedly a growing trend.

Still, they wrote that they believe AI provides opportunities for useful application “as long as the related epistemic risks are also understood and mitigated.” And when Google rolled out its AI chatbot Gemini earlier this year, it produced historically inaccurate images of people of color. The company paused and then relaunched the chatbot’s image-generation tool after public backlash. The chatbot misleading Moffatt with false information is an example of “botshit,” which describes incorrect or fabricated information produced by chatbots that humans use to complete tasks.

Most studies on technology adoption in financial services focus on Internet banking customers, with limited research on the insurance industry18. According to Ref.19, trust is important, but other factors, such as privacy concerns and perceived usefulness, are also critical for insurance chatbot usage. Also, Ref.20 observed that security and integration are challenges for conversational agents in the insurance industry; thus, the issue of privacy and integrity of the data in insurance chatbots should be an active research area13.

The labeled text data would then be run through the software’s machine learning algorithm. This would have trained the algorithm to discern the chains of text that a human might interpret as a policy-related question or a claims-related question, for example, as displayed in a text message. Amidst the vast array of I4.0 technologies that are currently being implemented in the insurance sector, this paper is focused on the use of chatbots, whose adoption began in approximately 2017. Voice assistants can be defined as conversational engines that engage in interactive dialog with individuals and are facilitated by artificial intelligence (AI) algorithms that simulate natural language (Rodríguez-Cardona et al., 2019).

Why LOOP chose Quiq’s AI bot – can customer service scale?

AI is emerging in the insurance industry and is being applied across multiple areas including the interpretation of data, business operations and driver safety. Strategies to improve driver safety are particularly timely as insurers attempt to strike a balance between the recent spike in auto accidents and increasing auto insurance rates. H20.ai developed the open-source machine learning platform software utilized by Progressive Insurance. H20.ai claims that its software is in use by 9,000 organizations and over 80,000 data scientists. To date, the California-based software company has reportedly raised $33.6 million in Series A and B funding.

According to a report by McKinsey, automation in document processing can reduce administrative costs by up to 60%, highlighting the significant cost-saving potential of this technology. Hyper-personalisation involves using data analytics and AI to tailor insurance products and services to individual customer needs and preferences. This approach enhances customer engagement, improves retention rates, and drives growth.

See if you can customize the chatbot to match your brand’s style and customer service needs. Also, look for services that provide templates and easy design tools to make the setup process easier. Evaluate the different types of chatbots, like rule-based, AI-powered, hybrid and voice-enabled chatbots.

Customers could activate the bot from the AA quotation page, both on the website and mobile app, and the bot in turn helped them navigate and interact with the form so they could find the right coverage at the right price. If a customer required more assistance, this chatbot for sales conversion could easily transfer them to a human agent via the bot integration with Zendesk, a live chat and CRM software. The chatbot can then purportedly send that response to the customer, or it can hold it until a human agent approves it. This might allow for a very small margin of error given that each time a human agent approves or disapproves of a response, the machine learning algorithm adapts to it.

Guide to Insurtech: The Types, Key Trends, and how AI is Impacting the Insurance Industry

In January 2017, Liberty Mutual announced plans to develop automotive apps with AI capability and products aimed at improving driver safety. Solaria Labs, an innovation incubator established by Liberty Mutual, has launched an open API developer portal which integrates the company’s proprietary knowledge and public data to inform how these technologies will be developed. An Application Program Interface or API is essentially a toolkit that provides the blueprint for building software applications. This improved use of data is consistent with one of the most important broad trends in AI and insurance (which we’ve written about in-depth previously).

This may require several clarifying questions, but soon Nina would navigate the user to the correct page of the site they were looking for, then ask if they can help with any decision making. For example, if someone is spending a long time on the same few pages of a website the assistant can pop up and ask if they need any help. The app gives financial recommendations during business trips such as which hotel to stay at and where to eat. These guests could include office guests such as interns, job candidates, or colleagues from overseas. It is important to note that the app requires anyone using it to create an account with a company email.

However, more recently, they have begun to offer a virtual assistant for customer service called Nina. Users can ask the virtual assistant questions with voice or text, and the company claims it can be integrated into a client company’s website or smartphone app. The Co-Pilot chatbot can purportedly find possible answers to customer questions automatically, and these answers are based on both historical customer service data. It can then send that response to the customer or withhold the response for a human agent to approve. IBM watsonx Assistant now supports this capability in conversational search, generating conversational answers grounded in enterprise-specific content to accurately respond to customer and employee questions.

Like Flo, insurance chatbots could reinforce marketing strategies in tandem with pleasing the customer. Allstate’s chatbot highlights easyDITA’s capacity for single-sourced information and immediate responses, which may be an asset for any chatbot built to cut down customer interaction times and maximize customer satisfaction. This paper tests the reliability of the TAM by Davis (1989) in explaining policyholders’ attitude toward the mediation of chatbots in their interactions with insurers. Although the reliability of TAMs for explaining fintech acceptance has been extensively demonstrated (Firmansyah et al., 2023), empirical analyses in the insurtech sphere are not common. Thus, this paper has expanded the empirical evidence in this novel field of the insurance industry.

• Their input was carefully considered to further fine-tune the questionnaire to its final version.
• For this particular scenario, I see this as an issue of regulatory compliance on the one hand, and accuracy on the other.
• The concept of “robot therapists” has been around since at least 1990, when computer programs began offering psychological interventions that walk users through scripted procedures such as cognitive-behavioral therapy.
• These can be saved with chatbots handling repetitive tasks of reviewing insurance claims, appointment scheduling, analyzing test results, etc.
• Thus, currently, conversational robot technology should be regarded as a supplementary channel in a company’s communication with a customer, one that could offer enhanced service in very specific circumstances.

On top of that, nearly three-quarters of consumers expect a response from brands within 24 hours or less, meaning companies that fail to do so will inevitably fall behind. Figure 12 shows when the user has been given rights to access the Human Resource chatbot. All interactions with the chatbot, including query processing results, are stored in the log file for auditing purposes. Figure 11 shows when the user has been given rights to access the Commercial Lines chatbot.

Through the use of contextual knowledge and intelligent content, ABIE is able to address what coverages work best for certain businesses, what incidents each coverage covers and more. HF Reveal NLP serves as an engine for their risk adjustment solutions for both healthcare plan networks and providers. Health Fidelity focuses on risk adjustment and offers a thorough process for adjusting risk from every angle.

The authors in Ref.17 stated that chatbots’ security and privacy vulnerabilities in the financial sector must be considered and analysed before the developers do the deployment. Through an analysis of the literature, the researchers identified the security issues but did not provide a framework or methodology for identifying the security threats in chatbots. The findings reveal that the social-emotional characteristics of chatbots in the financial industry can indicate a discrepancy between privacy and trust. The authors concluded that suitable precautionary analysis concerning chatbots’ security and privacy vulnerabilities in the financial industry must be executed before deployment. Nuance Communications is more widely known for their AI-enabled voice recognition technology for healthcare.

• No matter how specific your prompts are, it will occasionally cite made-up sources and introduce outright errors.
• In turn, AI users must adopt a risk management policy and program overseeing the use of high-risk AI systems, as well as complete an impact assessment of AI systems and any modifications they make to these systems.
• Indeed, trust forms the core of the insurance industry (Guiso, 2012), given its inherent challenges of moral hazard and adverse selection.
• The study identified security challenges and suggested ways to reduce the security challenges that are found with chatbots.
• For that price, the chatbot can send voice messages and even do live video calls with the bot in human-like form on the screen.

The software would then be able to scan through a new claim application form and extract each data point from each of the sections. A customer service agent who may be speaking to the customer on the phone could then search for past claims that are similar to the client’s. The software would then provide the user with the option to open the list of those documents, find trends, and find possible causes. This would allow them to easily manage the data for verification through the client company’s specific procedures. We can infer the machine learning model behind Watson Explorer needs to be trained on tens of thousands of their client’s insurance claims.