PortfoPlus brings ChatGPT to insurance agents Digital Finance

One of driverless car company Nauto’s goals is to help commercial fleets avoid collisions by reducing distracted driving. The company’s AI-powered driver safety system — which boasts dual-facing cameras, computer vision and proprietary algorithms — assesses how drivers interact with vehicles and the road to pinpoint and prevent risky behavior in real time. 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. 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.

The traditionally cautious insurance sector now widely accepts AI as a powerful tool for cost reduction, growth, operational efficiency and employee satisfaction. This transformation is being driven in large part by a new category of companies known as “insurtechs,” which excel at leveraging AI, data analytics and industry data lakes to gain a competitive advantage. While AI chatbots are still in their early stages, purpose-built AI solutions in insurance offer tangible benefits in claims management, underwriting and other crucial areas of the insurance value chain. Insurance companies can choose how they embrace AI solutions with CognitiveScale’s Cortex AI Platform.

As a result, the advertising and marketing sectors are experiencing a paradigm shift with the integration of generative AI. They are seeing unprecedented levels of personalization, content creation, and customer engagement. Knowji uses generative AI to create personalized vocabulary lessons, adapting to the learner’s proficiency level and learning pace. By generating custom quizzes and employing spaced repetition algorithms, Knowji ensures effective retention and mastery of new words, making language learning more efficient and tailored to individual needs.

Therefore, our conclusions must be taken with care to be extrapolated to policyholders from countries with nonenclosed cultures and/or persons with dissimilar profiles with regard to professional and educational status. To obtain more accurate conclusions, extending the countries represented in the sample and socioeconomic profiles is needed. The significant impact of trust on attitude and BI is in accordance with mainstream reports. In the field of customer acceptance of chatbots, we can outline Kasilingam (2020), Kuberkar and Singhal (2020), Joshi (2021), Gansser and Reich (2021) and Pitardi and Marriott (2021).

Once the structural model and its measurement have been stated and the final sample is available, we estimated the model in Fig. Internal consistency is checked with Cronbach’s alpha, a composite reliability measure (CR), Dijkstra and Henseler’s ρA, which must be above 0.7 and with an average extracted variance (AVE) that is expected to be higher than 0.5. We analyzed the discriminant capacity of the scales with the Fornell-Larker criterion and heterotrait-monotrait (HTMT) ratios. Whether you’re starting with a blank canvas or using a template, the first steps are the same. Venture capital investment also remains robust, signalling a strong belief in the transformative potential of insurtech startups. The insurtech landscape has undergone significant transformation in recent years, propelled by technological advancements and evolving consumer demands.

Re-wiring financial services operations for a bold future

According to the technology acceptance framework, trust is supposed to impact attitude or BI directly but is also mediated by PU and PEOU. Therefore, trust must be a keystone factor in explaining insurtech adoption (Zarifis and Cheng, 2022). It is expected that the digitalization of claim management processes will reduce the number of human operators linked with this insurance process by 70%–80% by 2030 (Balasubramanian et al., 2018).

In summary, we observe that none of the previous studies have focused on threat modelling for insurance chatbots. Also, no study has used STRIDE modelling to identify security threats that pertain to insurance chatbots. STRIDE, as the oldest and most mature threat modelling method25, has the capabilities to afford reliable, proactive security assessment of insurance chatbots. Thus, as a contribution, this paper presents a first attempt at threat modelling for insurance chatbots using STRIDE. It also presents an empirical study from the South African industry, which is a geographical context not yet covered in the literature to date.

Natural Language Processing in Banking – Current Applications

The algorithm used health costs to determine health needs, but that reasoning was flawed. “Less money is spent on Black patients who have the same level of need, and the algorithm thus falsely concludes that Black patients are healthier than equally sick White patients,” according to the study. One means the chatbot unleashes its full creativity, making it far more engaging and a lot of fun, but prone to errors and fabrications. “This will be a revolutionary sales assistant because it can help agents connect with clients and complete sales,” Ko said. Wong has worked both as a salesperson at an insurance broker as well as an engineer at IBM, after studying computer science at Hong Kong University. He and his co-founders Phoenix Ko (pictured, left) and Hugo Leung (right) got the startup bug.

According to a North Highland survey (via Consulting.us), 87% of leaders surveyed perceived CX as a top growth engine. Emplify research found that 86% of consumers would leave a brand they were previously loyal to if they had just two or three bad customer service experiences. An Accenture study from 2018 found that 91% of consumers are more likely to buy from brands that recognize, recall and provide relevant offers and recommendations. It’s only been about two months since the launch (as of the time of this writing), but we can already see how much ChatGPT impacts our experience.

The software would then be able to comb through a customer’s claim application form and extract the important information from it for the insurance broker processing the claim. This information would be accessible from a user dashboard that displays the claim itself and the information extracted from the software. We’ve covered what car insurance companies are up to with AI, but what does it mean for the typical driver? Let’s talk about how the use of AI could affect customers in the short term and the more distant future. Car insurance companies have been using AI to streamline existing processes, reduce costs and minimize the hours of human labor needed. Banks could train chatbots to provide rapid and effective customer care by answering common questions and fixing simple issues.

The most pivotal scenario arises during the communication of a claim, considering that the primary aim of an insurance contract is to shield the policyholder from the economic fallout caused by adverse events (Guiso, 2021). 1-800-Flowers, the biggest gifting retailer in the US, uses AI to make shopping a breeze. Their virtual assistant, GWYN (gifts when you need them), helps users find the perfect gift with smart, contextual suggestions.

We can infer the machine learning model behind Watson Explorer needs to be trained on tens of thousands of their client’s insurance claims. Each claim would be labeled according to the sections of the claim application form, and by the terminology ChatGPT App that commonly is filled into it. Then IBM or a data scientist at the client company would expose the machine learning algorithm to this labeled data. Two people who have the same facts on paper can still present different risk profiles.

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This is where you’ll define the canonical forms and dialog flows that are specific to your insurance customer support center chatbot. You can foun additiona information about ai customer service and artificial intelligence and NLP. Now that you have a foundational understanding of Nemo-Guardrails and its capabilities, you’re well-prepared for the next section. The upcoming tutorial will exclusively focus on alternative methods for configuring your LLM, particularly useful for those who are looking to use providers, such as Azure.

Our “top 4” rankings are based on the National Association of Insurance Commissioners’ 2016 ranking of the top 25 insurance companies. Our team can help you dight and create an advertising campaign, in print and digital, on this website and in print magazine. If you look at our progress, since we started effectively in 2014, to be able to reach a mid-market position in the span of 10 years, I think it’s something which is quite an achievement. We were chosen as their exclusive life insurance partner over the established market players. We pride ourselves on our digital capability and our agility in being able to do much to achieve. But research also shows some people interacting with these chatbots actually prefer the machines; they feel less stigma in asking for help, knowing there’s no human at the other end.

Elsewhere, Conversica recently raised $31 million to grow its conversational AI assistant that helps sales teams convert in-bound leads through automated conversations, while LivePerson snapped up conversational AI startup Conversable. Chatbots are one of the most talked-about uses of natural language processing (NLP) software in business. Some of the most common application areas for chatbots include customer service, healthcare, and financial advisory. First, they can analyze customer data to understand their preferences and needs and use this information to provide personalized customer service and support to users by addressing their queries and concerns in real-time. Banks could also use AI models to provide customized financial advice, targeted product recommendations, proactive fraud detection and short support wait times.

We ensure we have a base layer of recognised security certifications, from Cyber Essentials and PCI certifications to international best practice such as IASME Governance. We are audited by external third parties in these areas and continually review and update processes, policies and our systems. Disclaimer — I will be using the terms “RAG tool”, “Q&A system”, and “QnA tool” interchangeably. For this tutorial, all refer to a tool that is capable of looking up a bunch of documents to answer a specific user query but does not have any conversational memory i.e. you won’t be able to ask follow-up questions in a chat-like manner. However, that can be easily implemented in LangChain and will likely be covered in some future article. The insurance sector has embraced AI to improve customer service, increase profitability and address the talent gap.

For the insurance industry, chatbots are a powerful tool that can significantly reduce costs for providers. Middle East-based insurance firms like Qatar Insurance Company and Oman Insurance Company have adopted the technology in 2021, extending their service to platforms like WhatsApp for greater customer reach. 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.

Threat modelling

Vitality is a behavior change platform launched by the South African insurer Discovery, and it’s also present in the US and the UK. Customers buying Vitality Heat insurance get a deal on an Apple Watch and can collect “activity points” for walking, insurance chatbot examples running or having their blood pressure checked. The program is targeted at employers who want to improve the health of their teams. Wearables and telematic devices collect and send data about customers’ lifestyles or driving habits.

These tools leverage machine learning to identify vulnerabilities, predict potential threats, and provide actionable insights to mitigate risks. By detecting anomalies and patterns indicative of cyber threats, these tools can provide early warnings and recommendations for mitigating risks. For example, Beazley’s cyber risk assessment platform uses machine learning to analyse clients’ IT infrastructure and identify vulnerabilities, helping businesses strengthen their cyber defences. Allstate’s business insurance chatbot was developed in collaboration with Jorsek for customer service to a base of small business owners. Allstate customers can access ABIE through their website, and ask insurance-related questions that a live agent would normally answer. UnitedHealth’s Agent Virtual Assistant – AVA for short – is the company’s foremost public product offering featuring AI applications.

It includes the information you fill out to get a quote — your address, driving history, vehicle information and age — and data like your credit-based insurance score. The program, called Snapshot, requires customer to install a device into their car’s diagnostics port or download an app to their mobile phone. As the customer drives, the device or app records information about the driver’s behavior and feeds it into a predictive analytics algorithm.

Developed in its alpha stage (as of August 2023), the toolkit aims to make LLMs trustworthy, safe, and secure by guiding their conversational behavior. Finally, let’s set up the ReAct agent using a prompt that emphasizes multiple thought-action-observation steps. Luckily for us, this is already available on the LangChain hub (you can also override this by defining your own). The prompt template requires three input_variables i.e. tools, input and agent_scratchpad. One of the greatest challenges that chatbot developers face is a diverse array of more than 30 recognised Arabic dialects. There are more than 60 ways that Arabic speakers can express the word ‘want’, posing a unique challenge to linguists and computer scientists collaborating on NLP project development.

In just two months after its launch, GPT-3-powered ChatGPT reached 100 million monthly active users, becoming the fastest-growing app in history, according to a UBS report (via Reuters). ChatGPT is a language model that uses natural language processing and artificial intelligence (AI) machine learning techniques to understand and generate human-like responses to user queries. Irrelevance detection models know when to pass a conversation along to a human agent, and entity-detection models let users speaking informal Arabic be understood more often. Studies show that the technology can cut global business costs by $1.3 trillion per year by reducing the resources spent on customer-service agents answering common questions. They expand a company’s reach, allowing customers to interact any time and from anywhere in the world, overcoming the limitations of traditional customer service hotlines based in a specific time zone.

This need for security has also risen in insurance, and numerous AI firms are selling claims fraud detection solutions to the insurance sector. Banks could also create chatbots with the capability to submit insurance claims and get information about the claims procedure. In a global market that makes room for more competitors by the day, some companies are turning to AI and machine learning to try to gain an edge. Supply chain and inventory management is a domain that has missed some of the media limelight, but one where industry leaders have been hard at work developing new AI and machine learning technologies over the past decade. However, there is some resistance to AI as autonomous vehicles are expected to reduce automobile accidents thus reducing the need for auto insurance. Daniel Burrus, noted author and strategic advisor on tech innovation to leading insurance companies argues that the “risk is shifting” from the driver to the auto manufacturer and the companies that design the smart technologies.

Given the expanding layers of legislation at every level, we can expect the AI landscape to only become more complex in the near future. For the time being, there are several actions companies can take to help ensure they are protected. The pervasiveness of chatbots is due in part to the fact that they aren’t exclusive to just one industry. Rather, ChatGPT they can be customized for different use cases and tailored to a variety of businesses. Below, we’ve compiled a list of common chatbot examples and uses currently in place. 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.

And all of this would be available 24/7, making it easy for customers to get help by answering questions, resolving issues and providing financial education outside of regular business hours. Academic institutions change their values systems all the time, particularly around the use of language. Arguably, one of the most consistent, historically reliable, widely accepted system of ethics in existence belongs to the Catholic Church.

AI-powered chatbots and digital assistants can provide personalised assistance and support, addressing customer inquiries and concerns in real-time. For example, Aviva’s AI chatbot offers personalised policy information and recommendations based on individual customer profiles, improving the overall customer experience. By automating routine tasks such as policy renewals, claims processing, and customer inquiries, insurers can reduce operational costs and improve efficiency. According to a report by Accenture, AI-driven automation could save the insurance industry up to $300 billion annually by 2030. Credit card companies could make use of AI applications across multiple business areas. AI-based fraud detection is among the most widely discussed AI applications in the financial sector, and it seems to work for credit cards similarly to how it works for banks.

Rackspace Technology, a cloud computing company, surveyed 1,420 information technology professionals in February 2023. Writing about the survey’s subset of insurance pros for Insurance Thought Leadership in April, DeVerter said there was a 30% increase in new AI/ML projects from 2021 to 2022. 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). Through facts and figures, we aim to provide pertinent insights for business leaders and professionals interested in how machine learning is impacting the insurance industry. Kumba is an AI Analyst at Emerj, covering financial services and healthcare AI trends.