How insurance companies work with IBM to implement generative AI-based solutions

IBM watsonx Assistant transforms content into conversational answers with generative AI

Even if companies don’t provide data about factors like gender, race and income, AI could still find other factors that stand in for that data and have effectively the same outcome. If something like the time of day when driving is taken into account to build a car insurance model, that could be a proxy for income level. Progress Software and Health Fidelity have AI staff of similar sizes, but neither is as robust as that of IBM. The smallest company, Taiger, has yet to gain much traction with its NLP solution, but this may be because it has not had enough time with the first round of select customers leveraging the software now.

Healthcare Chatbots: When Do They Help and When Do They Hurt? – Built In

Healthcare Chatbots: When Do They Help and When Do They Hurt?.

Posted: Fri, 21 Jun 2024 07:00:00 GMT [source]

Advanced risk models powered by AI will play a crucial role in forecasting increasingly unpredictable weather events. Presently, Verisk’s AIR Worldwide provides a hurricane catastrophe model tailored for the US, alongside the First Street Foundation Wildfire Model. Lapetus Solutions works with industries like life insurance and medical underwriting to improve the overall assessment process.

Humans and bots can work together to keep customers happy, even as expectations climb. In this article, we’ll cover everything you need to know about customer service chatbots, including tips on implementing a bot strategy that sounds anything but artificial. As a contribution, this study deepens understanding of the application of STRIDE modelling. It also offers a case study on chatbot security regarding the insurance industry, which is a first attempt to the best of our knowledge.

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Whether you’re starting with a blank canvas or using a template, the first steps are the same. Researchers Ian P. McCarthy, Timothy R. Hannigan, and André Spicer coined the term in a paper published in January and a July 17 Harvard Business Review article. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Z.B — Investigation, Methodology, Data curation, Data analysis, Validation, Writing – original draft. D —Conceptualization, Methodology, Data analysis, Validation, Supervision, Writing – final draft.

Gallagher Bassett said 150 insurance businesses in North America, the United Kingdom, Australia, and New Zealand were surveyed. Participants comprised 85% insurers, with MGAs and MGUs accounting for 11%, and underwriting agencies constituting the remaining 4%. Figures 8, 9, 10, 11 and 12 give the second level of the data flow diagram decomposition of the iAssist chatbot’s five business operations (User Login, Claims, Personal Lines, Commercial Lines, and Human Resources). Figure5 shows the contact centre agent, employee, and administrator as the actors for the iAssist use case.

Depending on the complexity of my instructions, I’ve found it sometimes also returns the names of its sources. If I can’t find any mention of them from a quick Google search, they’re likely made up. Generative AI is changing the insurtech space for 2024, and financial marketers should pay attention.

Technology

Some people have turned to ChatGPT to outsource mundane work tasks or even help them land new jobs. In a survey of 4,500 employees conducted by professional social networking app Fishbowl earlier this month, nearly 30% reported having already used ChatGPT to assist with work-related tasks. Suumit Shah, the CEO of the e-commerce platform Dukaan, tweeted earlier this month that “we had to layoff 90% of our support team” because an AI chatbot was able to do its work faster. A recent Goldman Sachs report on the state of AI found that generative-AI tools could lead to a “significant disruption” in the labor market and affect 300 million full-time jobs worldwide. White-collar workers — particularly people in law and those part of an administrative staff — are most likely to be affected by new AI tools, Goldman found. Nigam, the CEO of the avatar-tools company, expects to make decisions on who gets promoted based on how well they know ChatGPT, he told Insider.

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 analysis, enabling insurers to develop more tailored and competitive products. NLP technology enables chatbots to understand and process natural language inputs, allowing them to interact with customers in a more human-like manner. For example, Aviva’s AI chatbot can understand complex policy inquiries and provide detailed explanations, enhancing the overall customer experience.

Air Canada’s customer service chatbot told Moffatt he could claim the discount after the flight. Yet, the company later denied his discount request because they said it had to be filed prior to the flight. Figure 14 shows when the user has been given the right to access the WhatsApp chatbot. All the interactions with the chatbot, including query processing results, are stored in the log file for auditing purposes. Nick Patrick, the owner of the music-production company Primal Sounds Productions, told Insider he used ChatGPT to fine-tune legal contracts for clients.

• This means that developers have to share certain information with deployers, including harmful or inappropriate uses of the high-risk AI system, the types of data used to train the system, and risk mitigation measures taken.
• Then, the user requests information and asks FAQ (frequently asked questions) related to the claim.
• IBM watsonx Assistant’s conversational search functionality builds on the foundation of its prebuilt integrations, low-code integrations framework, and no-code authoring experience.
• Figure 9 depicts when the user has already been given rights to access the Claims chatbot.

AI bias also presents a danger when it comes to recruitment, potentially discriminating against people who are from certain regions or socio-economic backgrounds. For these reasons, there is still a critical need for human oversight of AI decisions to ensure inclusivity, fairness and equal opportunity. There are, however, multiple risks that can arise when using AI — primarily because it can easily generate errors. For example, AI can ingest statute information from one U.S. state and posit that it applies to all states, which is not necessarily the case.

For example, AXA uses AI algorithms to analyse customer data and provide personalised policy recommendations based on individual risk profiles and coverage requirements. The integration of IoT and telematics in insurance is enabling the development of usage-based insurance (UBI) models. These technologies provide real-time data on vehicle usage, driving behaviour, and environmental conditions, allowing insurers to offer more personalised and fairer premiums. This trend is particularly prominent in auto insurance, with companies like Allstate and Progressive leading the charge. Tesla’s embedded insurance offering, which provides real-time premium adjustments based on driving behaviour, is a prime example of how this trend is playing out. Such models not only enhance customer convenience but also provide insurers with more accurate risk assessments.

AI also can be biased if it uses data that could be inherently prejudiced and creates algorithms that discriminate against a group of people based on, for example, ethnicity or gender. This could result in the AI recognizing that one racial or ethnic group has higher mortality rates, and then inferring that they should be charged more for life coverage. It has limitations, such as errors, biases, inability to grasp context/nuance and ethical issues. Insider also pointed out that AI’s “rapid rise” means regulation is currently behind the curve.

• But it’s the constraints of Quiq’s bot that make it well-suited for a service bot use case, particularly in a regulated industry.
• Customer satisfaction has dropped, and more customers aren’t using the self-service options, or worse, trying and failing and ending up escalating the issue to traditional support channels.
• With this solution, customers can purportedly do this without an agent and would save time for themselves and the client company.
• In Ref.9, it was stated that security and privacy in chatbots require serious attention.

In a recent survey from the job site Resume Builder of business leaders who were hiring, 91% of respondents said they wanted to bring on workers who knew how to use OpenAI’s chatbot to save time and enhance productivity. Companies across various industries — including healthcare, education, and insurance — are looking to hire workers with experience using AI. High-risk care management programs provide trained nursing staff and primary-care monitoring to chronically ill patients in an effort to prevent serious complications. But the algorithm was much more likely to recommend white patients for these programs than Black patients. In November 2021, online real estate marketplace Zillow told shareholders it would wind down its Zillow Offers operations and cut 25% of the company’s workforce — about 2,000 employees — over the next several quarters. The home-flipping unit’s woes were the result of the error rate in the ML algorithm it used to predict home prices.

As we started exploring what we could do together, it felt like we could figure out a way to use our own Help Center [documentation] to answer customers through the bot. Earlier this year, Colorado became the first state to pass comprehensive legislation to regulate developers and deployers of high-risk AI to protect the consumer. High-risk AI systems are those that have a substantial input into consequential decisions relating to education, employment, financial or lending services, essential government services, healthcare, housing, insurance, or legal services. The use of chatbots has greatly improved the healthcare system; there is no doubt about this fact. Healthcare chatbots are vital for improving the efficiency of a healthcare organization in terms of analysis, scheduling, organizing abilities, communicative skills and more.

This question is especially relevant in I4.0 technology, which is a very dynamic and active field in rapid and continuous growth and improvement. (2) The use of big data analysis tools, which are based on deep learning and machine learning, to evaluate all the data available by insurance companies may allow more accurate learning to predict fraudulent claims (Rawat et al., 2021). Agarwal et al. (2022) indicated that these tools allow the identification of 30% more irregular claims than conventional analytic tools. Industry 4.0 (I4.0) is strongly impacting the economy, businesses, and society (Tamvada et al., 2022).

Ethical approval

Shannon Ahern, a high-school math and science teacher, said she used the AI chatbot to generate quiz questions and lesson plans. Derek Driggs, an ML researcher at the University of Cambridge, together with his colleagues, published a paper in Nature Machine Intelligence that explored the use of deep learning models for diagnosing the virus. For example, Driggs’ group found that their own model was flawed because it was trained on a data set that included scans of patients that were lying down while scanned, and patients that were standing up.

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. 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). Many chatbots are confined to handling rudimentary interactions; beyond that, their responses tend to lack substance due to reliance on scripted conversational trees and basic dialog datasets (Nuruzzaman and Hussain, 2020). Failed responses from conversational robots have a negative impact on users’ judgments regarding the adoption of this technology, consequently leading to increased resistance towards its utilization (Jansom et al., 2022).

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.

IBM’s conversational AI software is called IBM Watson Conversation, which uses the NLP engine within their Watson AI platform to create chatbots and serve customers. Woebot is a mental health chatbot app that tracks the user’s mood based on the information the user provides, as well as creates a safe place for the user to express their feelings. Woebot operates on the basis of Cognitive Behavioral Therapy (CBT) and uses NLP to weave together clinical information and a light-hearted tone that patients can appreciate. Liberty Mutual explores AI through its initiative Solaria Labs, which experiments in areas like computer vision and natural language processing. By conducting comparative analyses of anonymous claims photos, this AI tool is able to quickly assess vehicle damage and provide repair estimates post-accident. They often have to navigate, with limited resources, a stormy market made of customers, competitors, and regulators, and the interactions between all these actors make finding answers to business questions a complex process.

With ChatGPT setting off a new revolution in AI, we could just be seeing the start of AI in the financial industry as these companies find new ways to use this breakthrough technology. Image credit – Feature image – screen shot of Weslee Berke, Head of Customer Care, LOOP by Jon Reed – from customer video on Quiq.com. Image of Quiq’s LLM processing provided by Quiq for express permission tu use on diginomica. With Quiq AI Assistants, the one article, one answer constraint is a thing of the past, allowing all the information relevant to a question to be combined into an answer precisely matching the customer’s inquiry.

Artificial Intelligence is currently being deployed in customer service to both augment and replace human agents – with the primary goals of improving the customer experience and reducing human customer service costs. While the technology is not yet able to perform all the tasks a human customer service representative could, many consumer requests are very simple ask that sometimes be handled by current AI technologies without human input. Finn AI has a banking chatbot service which can also be enabled for multiple languages and sentiment analysis that allows the client company to detect the quality of the customer’s experience. Because of this, the chatbot has the ability to determine whether it gave a helpful response based on how the customer responds to it.