How Leading Insurtech Companies Make Use of AI Solutions such as: Fraud Detection, Hyper-Personalization, and Underwriting

Niccolo is a content writer and Junior Analyst at Emerj, developing both web content and helping with quantitative research. He holds a bachelor’s degree in Writing, Literature, and Publishing from Emerson College. Banks could explore ways to use AI to prevent fraud by monitoring user transactions and spotting unusual activity. Join over 20,000 AI-focused business leaders and receive our latest AI research and trends delivered weekly.

Generative AI in Sell The Trend can also help you create engaging product descriptions and marketing material based on current trends. Adobe Firefly is a collection of generative AI capabilities built within the Adobe Creative Cloud suite, including Photoshop and Illustrator. It allows users to create and alter images using text prompts, which dramatically improves creative process. Firefly uses machine learning algorithms to analyze and build links between texts and images, allowing users to create original artwork with only a few clicks. Unity ML-Agents is an open source toolset that allows game developers to train intelligent agents with machine learning.

Other companies are using mobile platforms and technology such as AI to offer innovative products, such as insurance for single items, and reach consumers who have up to now have not had access to insurance. UHG also prides itself on being the leader in its industry when it comes to using artificial intelligence as its digital healthcare offerings continue to advance. When the company introduced the Level2 digital therapy platform for diabetes patients in 2020, then-CEO David Wichmann promised that wouldn’t be the last time the company would incorporate AI into its products. 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. HF Reveal NLP serves as an engine for their risk adjustment solutions for both healthcare plan networks and providers.

This will offer you a more flexible and potentially advanced setup for your conversational models. As advancements in Large Language Models (LLMs) continue to revolutionize various applications, the challenge of ensuring their safe and secure deployment has never been more critical. Enter “guardrails,” a technology designed to mitigate risks and enhance the reliability of these models.

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. Clinc’s client banks can deploy these applications to various channels such as mobile, web, interactive voice assistants, and messengers. However, the nature of how multiple neural networks are set up for these chatbots is unclear. Customers can also request more specific claims information such as the payout amount or if the payout check has been mailed yet. Once this app provides the user with information regarding their symptoms and their most probable causes, the user can also seek professional advice directly through the app’s window.

Automating the Claims Process

According to a survey from The Economist Intelligence Unit, 77% of bankers believe that the ability to unlock the value of AI will be the difference between the success or failure of banks. In a 2021 McKinsey survey, 56% of respondents report AI usage in at least one function of their organizations. In May 2016, Liberty Mutual announced the launch of its $150 million venture capital initiative, Liberty Mutual Strategic Ventures (LMSV).

Omni focuses on streamlining onboarding and offboarding processes using generative AI to automate and customize communications, track important documents, and remove manual data entry. This allows a seamless integration for new hires and a smooth transition for exiting staff. HubSpot is a comprehensive CRM software that automates and uses AI to simplify sales operations.

The platform adapts to each learner’s pace and progress, generating exercises and conversations that target specific areas of improvement, making language learning more interactive and adaptive. Its gamification makes learning a new language fun, encouraging consistent daily practice. Developed by Dreamtonics, SynthesizerV is a cutting-edge synthesis software that accurately simulates the intricacies of human singing. SynthesizerV uses a deep insurance chatbot examples neural network-based synthesis engine and generative AI to create configurable, realistic vocals in several languages including English, Japanese, and Chinese. The software provides live rendering and cross-lingual synthesis, allowing music producers to create realistic vocal tracks without the need for a live singer. The study found that the algorithm used healthcare spending as a proxy for determining an individual’s healthcare need.

In fact, a shift to more digital services can save money in the long term, creating growth opportunities for travel insurers and innovative third-party tech providers. A travel insurer with a varied offering of digital services has a competitive advantage over companies that have not invested for the future. According to a Boston Consulting Group report, the disruptive technology change allows for slashing up to 10 per cent in premium costs and eight per cent in claims expenses.

The ChatGPT-based software can predict flyer behavior, including how much they are willing to pay and where they want to travel, which allows for optimized pricing instead of the older method, where a set rate was used for every block of seats. One of the arguments for the use of RAG is the potential to introduce latency from the retrieval time and infrastructure overhead of managing the data. Statton concurs adding that enterprises will need to pay close attention to the conscious and unconscious bias that exists in their documentation, and knowledge bases.

This approach reduces the need for extensive coding expertise and accelerates the development process. For insurers, low-code platforms offer a valuable tool for developing and deploying digital solutions quickly and efficiently. Hyper-personalization leverages a wide range of data sources, including customer demographics, behaviour patterns, and preferences, to create highly tailored insurance products.

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There is also concern that there is a lack of transparency in the AI tools – how AI decisions are made. Nayya guides individuals and companies through health benefits with a selection process that runs on AI technology. Customers begin by completing a 10-minute survey that considers factors such as a person’s age, health history and what types of benefits they prefer. After filling out this information, Nayya’s platform then matches each individual or group with a benefits plan that best aligns with their circumstances.

If it doesn’t, it will usually iterate a few times (i.e. trying one of the other available tools or its own logical reasoning) and finally return a sub-optimal answer. This allows adjusters and claims managers to proactively manage claims, focusing their efforts where needed. Everyone benefits from this approach; workers get the specific treatment they need sooner and recover from injuries more quickly, claims costs are reduced and management efficiencies are improved. AI models can now predict potential policy losses and claim directions due to their ability to consider numerous inputs simultaneously, like medical history, demographics, driving records, weather information and adjuster’s notes. The speed and accuracy of the trained AI models provide valuable insights for underwriters and adjusters, leading to better outcomes.

• “Until there is clarity, transparency around where the models were built, where they were developed or that they had been validated.
• It cut out the middleman manual intervention of customers having to speak directly with consultants by providing a self-service.
• Generative AI can improve procurement by automating operations such as supplier discovery, contract drafting, and purchase order generation, reducing manual labor and errors.
• Chatbots lack the ability to discern shifts in voice tone or changes in conversational context (Vassilakopoulou et al., 2023), often resulting in incomplete interactions as robotic shortcomings are frequent (Xing et al., 2022).
• The application of I4.0 technologies to the insurance industry creates value for the insurance company, and heterogeneous transformational capabilities are sources of competitive advantage (Stoeckli et al., 2018).

The bot offers personalized recommendations based on your past searches and budget. An example of customer engagement is a generative AI-based chatbot we have developed for a multinational life insurance client. The PoC shows the increased personalization of response to insurance product queries when generative AI capabilities are used. AI algorithms can automatically verify and validate policy applications, identify discrepancies, and ensure compliance with regulatory requirements. This streamlines the policy issuance process, reducing the time required to issue new policies and renew existing ones. For insurers, it represents a valuable opportunity to reach new customers and diversify distribution channels.

The customer only needs to relay the details of their situation, such as which car was damaged and at what time. Because customers can make transactions with Native Chat, the chatbot itself must be trained in a way that will allow it to offer transactions to the customer and process them accurately. To do this, the software likely categorizes each inquiry as a question, ask for help, or a need to file a claim. Digitally-native eCommerce businesses are used to working with their customer data in order to write copy for marketing campaigns, run PPC ads, calculate customer lifetime value, and make decisions based on core metrics within CRM dashboards. As they point out, if OptumIQ can at least collect enough information about patients to make their conditions easier to predict, it not only decreases costs for patients but also across their entire service pipeline. They also mention data analytics as the specific AI capability the OptumIQ platform enables across UHG’s entire technology stack.

The data is used to train neural networks to predict the probability of an accident. Customers opt-in for this program to earn a substantial premium discount, while insurers can estimate risk better. Insurtech startups and scale-ups such as Clover Health, Fabric, GetSafe, Trov, Lemonade, BIMA, Slice, neos, ZhongAn use AI to successfully challenge traditional companies.

IBM offers software called IBM Watson Explorer, which the company claims can help insurance companies access and organize text data to improve their customer service and claims processing. AI helps chatbots eliminate redundant questions and recognise when a conversation needs to be passed along to a human agent, saving time and reducing frustration for users. The emergence of large language model-based AI systems in the early 2020s enhanced this suitability due to their versatility and capacity to offer credible responses across a diverse range of topics.

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. Pana is a vendor that purports to combine AI-powered chatbots and human employees to help companies and individual professionals manage travel logistics and expenses. Single users can use the app for business trips, and companies can use it for assisting guests and planning events. In order to create an effective chatbot ChatGPT App that made use of all relevant aspects of Watson, the company can include the Tone Analyzer tool for sentiment analysis, and run the entire application from the IBM cloud platform. One of these chatbot software is called Finie Personal, which can process banking transactions and respond to questions and advice requests based on the customer’s banking history. It is likely that car insurance companies can set their chatbot up to intake the insurance information of other parties involved in the damage.

These devices monitor various parameters, such as mileage, speed, braking patterns, and driving environments. By analysing this data, insurers can offer premiums that reflect the actual risk posed by each driver, rather than relying on generalised risk factors. According to a report by Allied Market Research, the global UBI market is expected to reach $125.7 billion by 2027, growing at a CAGR of 23.7% from 2020. AI-driven data analytics is playing a crucial role in the evolution of underwriting processes. By leveraging synthetic data and advanced analytics, insurers can automate underwriting, leading to more accurate risk assessment and faster policy issuance. According to PwC, automated underwriting can reduce underwriting costs by up to 30%.

If you want to use tools that take more than 1 input (for instance Tool_PercCalculator), you will be better off using Open AI Tools agent or Open AI Functions agent. In this tutorial, we will be using LangChain’s implementation of the ReAct (Reason + Act) agent, first introduced in this paper. The key takeaway from the paper is that if we prompt the LLM to generate both reasoning traces and task-specific actions in a step-by-step manner, its performance on the task improves. In other words, we are explicitly asking it to have multiple thought-action-observation steps to solve a task instance instead of coming to the final answer in one single jump (which ultimately leads to reduced hallucination). Progressive insurers are embracing the transformative power of AI to improve their operations, attract customers and enter new markets. Let’s examine this paradigm shift and explain how insurers who are on the fence about AI can get started with it.

Ways Chatbots Can Improve Healthcare

For example, AXA uses AI algorithms to analyse customer data and provide personalised policy recommendations based on individual risk profiles and coverage requirements. 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.

We will begin by taking a closer look at UnitedHealth Group’s AI-backed virtual assistant service. The company is ranked 8th on the 2021 Fortune Global 500, has a market capitalization of $400.7 billion as of March 31, 2021, and roughly 300,000 employees. Health Fidelity does not list any past insurance clients by name on their website, but they have raised $19.3 million in venture funding and are backed by UPMC. Data from a collision can show how the insured person was driving and help piece the event together.

How a company transformed employee HR experience with an AI assistant

Your insurance company will instantly know which driving mode you chose and will adjust your pricing accordingly. Perhaps you’ll even have a daily variable rate that changes based on real-time driving data. Tom Chamberlain is the vice president of customer and consulting at hyperexponential, which makes pricing software for insurance companies. In a May 2023 article for Insurance Thought Leadership, he wrote about AI’s potential to make underwriting more efficient and reduce an insurer’s exposure to risk. According to Jeff DeVerter, chief technology evangelist at Rackspace Technology®, insurance companies have been increasingly adopting AI and machine learning in recent years.

Jake Moffatt consulted Air Canada’s virtual assistant about bereavement fares following the death of his grandmother in November 2023. The chatbot told him he could buy a regular price ticket from Vancouver to Toronto and apply for a bereavement discount within 90 days of purchase. Following that advice, Moffatt purchased a one-way CA$794.98 ticket to Toronto and a CA$845.38 return flight to Vancouver. In March 2024, The Markup reported that Microsoft-powered chatbot MyCity was giving entrepreneurs incorrect information that would lead to them break the law.

The nonsignificant impact of PEOU on PU does not contradict the relevant positive link between PEOU and attitude and BI mediated by PU reported in the literature. In the field of insurance new tech products, Huang et al. (2019) and de Andrés-Sánchez and González-Vila Puchades (2023) found this. All the scales are reflective constructs and were answered on an 11-point Likert scale.

“With the use of publicly hosted generative AI services, companies should also consider the risk of sensitive information leak. Employee may accidentally include proprietary and sensitive information in the prompts sent to these AI services.” In a McKinsey survey among US life insurance agents from January 2020, 90 percent of sales conversations and almost 70 percent of ongoing client conversations took place in person. The development of open-source frameworks drives the rise of AI in the entire insurance industry. Companies have also learned how to collect and process big data sets, which are shared among organizations – also across the sectors. The companies that can build on the common AI experience will have a better chance to succeed in digital transformation. BetterHelp isn’t an AI system (it does use AI to help match users with therapists, according to Behavioral Health Business), but its handling of privacy illuminates a troubling divide between the way people and companies are treated.

Below are some of the ways AI has reshaped the insurance industry, leading to benefits (and some challenges) for insurers and customers. Even though there is advancement occurring in progressing chatbot technology, chatbots are still unable to understand empathy due to the absence of genuine emotional intelligence. Image-recognition algorithms can successfully analyze pictures taken by the client. You can foun additiona information about ai customer service and artificial intelligence and NLP. So, provided that it is a standard claim, the agent doesn’t have to travel at all. According to a customer story presented by Dutch fraud detection company FRISS, Turkish insurer Anadolu Signorta reached 210% ROI within 12 months of using their platform.

Health insurance is a critical component of the healthcare industry with private health insurance expenditures alone estimated at $1.1 billion in 2016, according to the latest data available from the Centers for Medicare and Medicaid Services. This figure represents 34 percent of the 2016 National Health Expenditure at $3.3 trillion. In recent years, the demand for greater cybersecurity has risen even among the everyday citizen. This is especially true for one’s personal and financial information, which fraudsters are constantly finding new methods of breaching accounts to find.

We’ll focus on building a chatbot for an insurance customer support center that keeps the conversation focused on insurance topics. This approach allows you to directly pass the LLM configuration to Nemo-Guardrails. It’s especially useful for those who wish to use LLM providers that may not yet be fully supported in .yml configurations. For example, if you’re using Azure as your LLM provider, LangChain’s Chat model offers a way to integrate it seamlessly. However, what’s noteworthy here is its capability to perform currency conversions to USD before arriving at the ultimate conclusion that the budgets are indeed different. The true potential of agents is unlocked when we give it complex questions and more tools to work with as we will see next.

The customer experience is improved with the information and assistance they provide. Since they are able to answer the basic questions at the first point of contact, they help users establish trust in the organization and quicken the pace of the care delivery process. Both traditional players and insurtech disruptors leverage their strategies on big data analysis and machine learning models for customer service automation, claim processing, underwriting, or fraud prediction. Thus, this study makes a theoretical contribution by deepening the understanding of threat modelling and data security in insurance chatbots, which has not received sufficient attention in the literature. So far, most studies on financial chatbots are focused on banking instead of insurance. The few studies on insurance chatbots have investigated issues of adoption, design and development, and the imperatives for trust and privacy.

Customers can receive instant policy approvals and pricing information, enhancing their overall satisfaction. Additionally, AI-powered analytics can identify patterns and trends that may not be apparent through manual ChatGPT analysis, enabling insurers to develop more tailored and competitive products. Natural Language Processing (NLP) and AI-powered chatbots are revolutionising customer interactions in the insurance industry.

• KLM plans to expand BB’s services across more digital channels, including voice-based interactions.
• According to the accompanying announcement, the AVA application is explicitly mentioned as the product of an over $5 billion annual investment in data and technology by the UnitedHealth Group.
• We’ll focus on building a chatbot for an insurance customer support center that keeps the conversation focused on insurance topics.
• Those prompts include “order support”, “product support”, “shopping help” and “feedback”.
• Users could potentially make fund transfers to other accounts or to pay merchants through a chatbot.

The internet is full of examples of crazy prompts to which ChatGPT and other large language models (LLMs) often provide accurate and competent answers. People are rapidly adopting ChatGPT and similar models for uses such as content creation, programming, teaching, sales, education and so on. 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.

“The hype and promise is way ahead of the research that shows its effectiveness,” says Serife Tekin, a philosophy professor and researcher in mental health ethics at the University of Texas San Antonio. Algorithms are still not at a point where they can mimic the complexities of human emotion, let alone emulate empathetic care, she says. But before it does, ‘these solutions will need to be more widely embraced by the customer’. “Based on the examples we’ve seen so far, there hasn’t yet been sufficient data to suggest that the benefits we could give parametrically are necessarily of value to the customer at the time of the event. Apart from ReAct, LangChain supports other agents such as Open AI tools, XML, StructuredChat, Self Ask with Search, etc that I strongly encourage you to read about here. One key thing to note here is that ReAct agents can only support tools that can take only 1 input parameter (for instance, from the tools described above, it can support Tool_Length, Tool_Date, and Tool_Search ).

[It] can’t be put back in the bottle, and so, all we can do is try to use it in a way that’s responsible and thoughtful, and that’s what we’re trying to do,” says Ferranti. “We switched from a process where we had to wait days and have a manual assessment, to something that’s happening in a couple of minutes,” said Mahmal. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The data used for this study are not publicly available due to confidentiality, but are available from the corresponding author Olawande Daramola () on reasonable request. In this section, we present some recommendations based on the key findings of this study.