AI Chatbots Could Be Used to Start New Pandemic, Researchers Warn
As part of their customer service strategy, businesses usually implement these chatbots on their websites and social messaging platforms like Facebook Messenger and X (formerly known as Twitter) DMs. Self-service options like chatbots empower customers to solve problems on-demand, allowing reps to focus on more complex support needs. Likewise, it touts the ability to perform a variety of other functions such as adding required documents (for example, birth certificates), adding beneficiaries investigating insurance products and supplementing current coverage. All these capabilities are assisted by automation and personalized by traditional and generative AI using secure, trustworthy foundation models.
Chatbots can analyze the given data to recommend appropriate healthcare plans for users. With the advancements of AI in the healthcare industry, chatbots are able to comprehend users’ needs. The customer experience is improved with the information and assistance they provide.
According to the case study, Watson’s Explorer software reduced their client’s claims processing time from two days to ten minutes and saved 14,000 agents 3 seconds per call on average. Jorsek claims to have helped Allstate Business Insurance employees access data they required to quote and issue complex business insurance products. The company purportedly accomplished this by teaching the chatbot to pick up more data from customer interactions. In this work, out of the three dimensions of trust—cognitive, relational, and emotional—we have considered only the first two dimensions. This choice has been justified by the fact that the interaction between the policyholder and the insurer is sporadic and under the assumption that it is due to prosaic matters, such as reporting a minor claim. However, for certain significant events, such as the loss of a loved one or a substantial material loss, emotional trust in interactions with the insurance company could also be a relevant factor in the acceptance of conversational robots.
Additionally, Lemonade’s claims chatbot, Jim, can settle claims within seconds, while incumbents could take anywhere between 48 hours and over a year to settle home insurance claims. This study used the TAM developed by Davis (1989) to elucidate the BI behind utilizing conversational bots to engage with insurers concerning existing policy matters, such as providing information about claims. The results show that the low acceptance of chatbots can be explained by the use of TAM constructs, performance expectancy and ease expectation along with trust. Regarding RQ1, what is customers’ average intention to use and attitude toward using chatbots in communications with the company to manage existing policies (e.g., to notify a claim)? Therefore, trust must be a keystone factor in explaining insurtech adoption (Zarifis and Cheng, 2022). Models of technology acceptance and use, such as the TRA, TAM, and UTAUT, have been extensively employed to investigate the acceptance of chatbots among both customers and employees within implementing companies (Balan, 2023).
The study analysed potential security and privacy exposures in the chatbot architecture and discovered that the security community has not yet implemented comprehensive requirements for chatbot security. The researchers started by understanding how the existing chatbot architecture works by following the path that a message takes from the client module to the communication module, the response generation module, and the database module. However, they did not provide a framework for identifying chatbot security attacks and mitigations. So far, the literature has reported some studies on the security of chatbots used in the financial industry.
Artificial intelligence (AI) is taking nearly every corner of the business world by storm, and companies are finding new ways to use AI in finance. There’s no doubt the benefits outweigh the additional work of developing a robust AI protocol. By putting in place stringent guardrails, the insurance industry will reap the rewards of AI while remaining compliant within a quickly evolving regulatory landscape.
Scientific American maintains a strict policy of editorial independence in reporting developments in science to our readers. She is a former staff reporter at Nature, New Scientist and Science and has a master’s degree in molecular biology. If you’re enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. Riley said that Citi is “generating and reviewing hundreds of use cases” to figure out how the AI can be applied across the company.
It received over 15,000 customer questions by March of 2018, less than half a year after the chatbot became available. Also, their chatbot was built by Microsoft Azure, which signals trust in the legitimacy of the AI behind the chatbot. Additionally, the chatbot is linked to the company’s Facebook page with over 4,000,000 followers.
It also presents an empirical study from the South African industry, which is a geographical context not yet covered in the literature to date. Thus, this study extends the existing discussion on threat modelling through a case study from a new context. It also demonstrates the application of STRIDE modelling for threat elicitation for data security of insurance chatbots, which has not received sufficient attention in the literature.
He worries that mental health app developers who don’t modify the underlying algorithms to include good scientific and medical practices will inadvertently develop something harmful. In an experiment that Koko co-founder Rob Morris described on Twitter, the company’s leaders found that users could often tell if responses came from a bot, and they disliked those responses once they knew the messages were AI-generated. While some people may balk at the idea of spilling their secrets to a machine, LLMs can sometimes give better responses than many human users, says Tim Althoff, a computer scientist at the University of Washington. His group has studied how crisis counselors express empathy in text messages and trained LLM programs to give writers feedback based on strategies used by those who are the most effective at getting people out of crisis. In order to create a chatbot that can process entire transactions, Kinvey and their clients need to work to train it in a way that will allow it to offer the user certain transactions and them process them accurately.
The fact that the case study is also from the South African context constitutes an empirical contribution because case studies on chatbot security from developing countries, particularly Africa, are uncommon in the literature. The study’s findings regarding the security vulnerabilities and security threats that pertain to insurance chatbots are outlined in the following sections. Advances made in 2023 by large language models (LLMs) have stoked widespread interest in the transformative potential of gen AI across nearly every industry and corner of the business. The greatest concern is that chatbots could hurt users by suggesting that a person discontinue treatment, for instance, or even by advocating self-harm.
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. As they’re using the quotation engine to fill out the necessary information, a chatbot pops up on their screen and says, “Hey, can I help you with that? ” It then has a conversation with the customer using natural language and helps them to complete the quote, providing suggestions for how to lower the quote, and keeping the customer engaged all the way through to conversion of sale. What’s even better is that this chatbot can work around the clock to be available whenever that customer needs to get a quote estimate.
On the other hand, in the TAM, BI is the usage intention of the assessed technology (Davis, 1989). It is commonly accepted that a positive attitude toward a given tech positively influences BI (Fishbein and Ajzen, 1975; Davis, 1989). In the third stage, upon purchasing the insurance and remitting the premium, the customer transitions into a policyholder.
5 Examples of AI in Finance.
Posted: Tue, 20 Aug 2024 07:00:00 GMT [source]
The financial industry encompasses several subsectors, from banking to insurance to fintech. It’s a highly competitive industry, as banks and other operators constantly seek an edge over one another. Fraud is a serious problem for banks and financial institutions, so it shouldn’t be surprising that they’re embracing new technologies to prevent it. AI lending platforms like those of Upstart and C3.ai (AI -3.61%) can help lenders approve more borrowers, lower default rates, and reduce the risk of fraud. Generally, artificial intelligence is the ability of computers and machines to perform tasks that normally require human intelligence, such as identifying a type of plant with just a picture of it. 2024 will be a revealing year for enterprise LLMs; LOOP’s story demonstrates exactly why.
Paypal also claims to use ML for fraud detection and risk mitigation in their payments platform. They purport to use hundreds of identifying factors within each transaction to ensure no fraudulent activity can go on. This may also include chatbot interactions such as asking certain questions repeatedly within a certain timeframe. While there are likely an equal amount of minor identifying factors within payments and chatbot conversations, it is unclear whether these conversations are also used to track fraud. Watsonx Assistant now offers conversational search, generating conversational answers grounded in enterprise-specific content to respond to customer and employee questions. Conversational search uses generative AI to free up human authors from writing and updating answers manually; this accelerates time to value and decreases the total cost of ownership of virtual assistants.
In 2017, The Economist declared that data, rather than oil, had become the world’s most valuable resource. Organizations across every industry have been investing, and continue to heavily invest, in data and analytics. Scientific American is part of Springer Nature, which owns or has commercial relations with thousands of scientific publications (many of them can be found at /us).
A similar example includes an algorithm trained with a data set with scans of chests of healthy children. Unveiled in October 2024, MyCity was intended to help provide New Yorkers with information on starting and operating businesses in the city, as well as housing policy and worker rights. The only problem was The Markup found MyCity falsely claimed that business owners could take a cut of their workers’ tips, fire workers who complain of sexual harassment, and serve food that had been nibbled by rodents. Limbic, which is testing a ChatGPT-based therapy app, is trying to address this by adding a separate program that limits ChatGPT’s responses to evidence-based therapy. Harper says that health regulators can evaluate and regulate this and similar “layer” programs as medical products, even if laws regarding the underlying AI program are still pending. We have an opportunity to make products that are not just convenient but truly helpful and effective for our customers.
While finance will always require a human touch and human judgment for some decisions and relationships, organizations are likely to outsource more work to AI algorithms and other tools like chatbots as the technology improves. C3.ai says its smart lending platform helps financial institutions streamline their credit origination process and reduce borrower risks. For example, it promises a 30% reduction in the time required to approve a loan applicant. It’s also achieved a $100 million increase in application volume and loan acceptance yield. Machine learning, which means the ability of computers to teach themselves things using pattern recognition from the data they sample, might be the best-known application of artificial intelligence. This is the technology that underpins image and speech recognition used by companies like Meta Platforms (META 1.74%) to screen out banned images like nudity or Apple’s (AAPL -0.39%) Siri to understand spoken language.
(PDF) The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review.
Posted: Mon, 09 Dec 2024 08:00:00 GMT [source]
“You really got to find time to, like, learn this skill,” Nigam previously told Insider. Others have used the chatbot to write listings for luxury real estate, assist in recruiting efforts, draft social-media posts, and develop code. Since OpenAI launched ChatGPT, its conversational AI chatbot, in November, many have used it to lose weight, plan vacations, and even land dates.
As a result, the LLM is less likely to ‘hallucinate’ incorrect or misleading information. Low-code platforms also support scalability and flexibility, allowing insurers to adapt to changing market conditions and customer requirements. By enabling rapid prototyping and testing, insurers can experiment with new ideas and iterate quickly, driving continuous improvement and innovation. By using synthetic data, insurers can test and refine their underwriting models without relying solely on historical data, which may be limited or outdated. Similarly, if a customer wants to make a change to their policy, chances are they don’t need to talk to somebody to do so.
Digital Genius claims their chatbot software “Co-Pilot” helps businesses automate the most frequently asked customer support questions. The software can also integrate with other management tools such as Salesforce and Zendesk, which could assist customer service agents in finding answers to customer questions. Natural language processing (NLP) could make this possible because the chatbot would be able to parse the words within a customer’s question. 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. Industry 4.0 profoundly impacts the insurance sector, as evidenced by the significant growth of insurtech. One of these technologies is chatbots, which enable policyholders to seamlessly manage their active insurance policies.
The company claims the chatbot uses the customer’s most recent transaction data in order to provide up-to-date and accurate responses based on the customer’s financial situation. Insurance companies can choose how they embrace AI solutions with CognitiveScale’s Cortex AI Platform. It facilitates the creation of AI applications, so clients can easily build models and apps that fit their specific business needs.
Although it’s clear that AI is only beginning to make its way into the insurance sector, Accenture estimates that big investments in AI could increase the annual profitability of insurers by 20%. Venture capital investment also remains robust, signalling a strong belief in the transformative potential of insurtech startups. In an effort to explore the ability of computer vision to identify distracted drivers, State Farm launched an online competition in 2016. The competition resulted in 1,440 participants and the company offered a total of $65,000, divided into 3 prize levels. Before we begin exploring each company, we’ll present the common patterns that emerged throughout our research in this sector.
The move, announced in February, is part of the investment bank’s effort to comply with the company’s policy to limit its usage of third-party software, according to CNN. Telecommunications giant Verizon announced that ChatGPT “is not accessible from our corporate systems” in an effort to limit the “risk of losing control of customer information” and source code, according to a Verizon press release from February. “Artificial streaming is a longstanding, industry-wide issue that Spotify is working to stamp out across our service,” a Spotify representative told Fox Business. While some companies are hiring employees with ChatGPT expertise, others are putting the brakes on integrating the AI into their employees’ workflows because of privacy concerns over feeding the tech confidential data. These sets would correspond to individual languages, and the data science team working on the model would have to make sure it can discern when a word is used in different languages.
We can infer the machine learning model for their chatbot software would need to be trained on hundreds of thousands of snippets from text chat conversations from customers. These conversations would involve asking insurance related questions, requesting help, or filing a claim. Each phrase, term, and the relevant sentence would be labeled according to which category of support request it is, and a data scientist would expose the algorithm to this data after it was labeled. This would train it to discern the chains of text that humans perceive as a customer service request and which sets of data the request refers to. In addition to the constructs inherent to the TAM, a factor that proves to be particularly significant in the analysis of the utilization of artificial intelligence technologies is trust (Mostafa and Kasamani, 2022).
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