Natural Language Processing in Insurance Current Applications Emerj Artificial Intelligence Research

Kinvey Native Chat is a chatbot made by Progress Software for insurance companies to use to build a chatbot for customer self-service transactions. Woebot is a mental health chatbot app that tracks the user’s mood based on the information the user provides, insurance chatbot examples 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.

Indeed, trust forms the core of the insurance industry (Guiso, 2012), given its inherent challenges of moral hazard and adverse selection. Policyholders’ trust in insurance companies is the perception that their services may enable fast and reliable recovery of casualties and that interactions between them will be satisfactory (Guiso, 2021). Mastercard’s KAI is like a conversational chatbot for sorting out an often tedius task—financial planning. It gives personalized financial advice, helps with card services in real time and lets you check your account info and purchase history. Customers can use it to chat with merchants and make payments without switching apps, making managing money easier for younger, tech-savvy users who expect a smooth retail experience. KLM’s conversational bot, BlueBot (BB), is a game-changer that boosts customer engagement, loyalty‌ and satisfaction.

You can run with it as is or add additional rules and completely customize the copy so the bot sounds and feels more on-brand. If you’re starting from scratch, you’ll need to build out your own script and decision tree based on “Bot Says” this and “User Clicks” that logic. In the Bot Builder, you can create a chatbot from scratch or use a template to help you get started. Chun said ChatGPT is good at summarizing information within a fixed perimeter but not necessarily accurate when incorporating background and context to provide insights. Hence, early adoption is more appropriate for internal information searches or summarizing legal documents. Cowbell’s co-founder and COO, Trent Cooksley, about the challenges and opportunities of cyber insurance.

Cross-cultural research on effectiveness of chatbot therapy is still sparse

In the past few decades, insurance companies have collected vast amounts of data relevant to their business processes, customers, claims, and so on. This data can be unstructured in the form of PDFs, text documents, images, and videos, or structured, organized and curated for big data analytics. In this article, we discuss how and where banks are using natural language processing (NLP), one such AI approach—the technical description of the machine learning model behind an AI product. 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.

When carriers receive documents from agencies, SubmissionLink analyzes these materials and locates essential data points for underwriters. Meanwhile, ClauseLink reviews insurance clauses to help providers compare their policies to those of competitors. Healthcare chatbots can offer users info about nearby healthcare facilities, hours of operation and nearby pharmacies. They can also be programmed to answer simple questions about a particular condition, such as what to do during a crisis or what to anticipate during a procedure. The pandemic has put to a test the already overextended healthcare system, with new value-based and telemedicine models. Global lockdowns have accelerated digital transformation in all industries, forcing even the most reluctant customer groups to embrace remote and mobile solutions.

Automating Invoice Processing

The insurance industry is making use of various artificial intelligence applications to solve business problems, but perhaps the most versatile is predictive analytics. The ability to aggregate data from disparate sources for business intelligence allows business leaders in insurance to inform important decisions across departments. We can infer that the machine learning model behind HF Reveal NLP was trained on tens of thousands of clinical documents and health insurance claims.

We also embed in our products the regulations, tailor-fitting and dealing with customers. “Mental-health related problems are heavily individualized problems,” Bera says, yet the available data on chatbot therapy is heavily weighted toward white males. You can foun additiona information about ai customer service and artificial intelligence and NLP. That bias, he says, makes the technology more likely to misunderstand cultural cues from people like him, who grew up in India, for example.

“The effect of out-of-pocket payments can be catastrophic to families; it drags them into a poverty trap,” the report said. As a result, Africa is lagging behind the global average insurance penetration of 6%, according to a report by Strathmore University in Nairobi, Kenya. Insurance penetration is the ratio of the total value of premium compared to gross domestic product (GDP). Kenya’s insurance penetration is 2.4%, Uganda’s is less than 1% and Tanzania’s insurance penetration rate is among the lowest in Africa, at 0.5%, according to the most recent figures cited in the Strathmore report. The app gives financial recommendations during business trips such as which hotel to stay at and where to eat. It can also help find the best flight to take given the dates of the business trip.

Artificial intelligence in Health Insurance – Current Applications and Trends

It is because of this that the chatbot is likely built to attempt to find a positive “version” of the user’s outlook and recommend it in a non-threatening way. Morgan and Anthony J. Drexel who came together to finance Thomas Edison’s research and merge their companies together. This could range from details about the damage to a car or house, to the number of lost items in a storm, to injuries sustained in a car accident.

About nine out of every 10 healthcare executives said in the same survey they will need to team up with a health services company with data and analytics expertise to more effectively implement AI in their operations, not a tech company. Fortunately for UnitedHealth Group, they have such a partnership in their own group. In our second use case, we explore the AI capabilities of Optum, United Health Group’s resident tech-based subsidiary, and OptumIQ, their patient data centralization platform. Of the four companies examined in this report, only Progress Software did not have any case studies available showing success with their software. That being said, the other companies only had one case study for their NLP solution for insurance.

AI doesn’t just speed up decision-making processes — it can also discover patterns among claims in certain locations or demographics that humans might not spot. Many large insurers are finding ways to digitize parts of their business process in preparation for future projects involving machine learning. This is especially true in claims processing, which could become faster and less error-prone if claims adjusters did not have to search through large amounts of data or paper documents manually. McKinsey research estimates that by 2030, more than half of current claims activities could be replaced by automation. AI algorithms can create risk profiles and automated customer service apps handle most policyholder interactions through voice and text, following self-learning scripts that interface with the claims. As an assistant for human providers, Insel says, LLM chatbots could greatly improve mental health services, particularly among marginalized, severely ill people.

Grab exploring tokenized payments for its users

The software likely has a confidence interval on how likely the categorization was correct. It would also be programmed to take certain actions depending on that confidence interval, such as directing the user to the correct page for filing claims. Under a certain percentage, the chatbot would route the message to a human agent for review. Health Fidelity offers software called HF Reveal NLP, which they claim is a natural language processing engine that enables many functions in the risk management software they offer to insurers. They also claim the software can handle unstructured data such as written notes in clinical documents, which has helped past clients use data they could not before. 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.

Uber Eats is working on an AI-powered chatbot that’ll ask users about their budget and food preferences to give personalized recommendations and speed up ordering. This AI chatbot is part of Uber’s broader use of AI, which is already used to match customers with drivers. It’s easy to forget that just a few decades ago, the practice would have sounded like something straight out of a science fiction novel. Now, leading brands are supplementing their care approach to scale their operations, providing customers with high-quality support, faster.

3 min read – Businesses with truly data-driven organizational mindsets must integrate data intelligence solutions that go beyond conventional analytics. While the potential of ChatGPT is promising, it is not without its risk in a practical business environment. Reports, tips, and tools to help your agency and business succeed and stay secure. Over the last two decades, out-of-pocket health payments as a portion of all health expenditures in sub-Saharan Africa rose from 40% in 2000 to over 60%, according to a UN analysis.

Industry 4.0 (I4.0) is strongly impacting the economy, businesses, and society (Tamvada et al., 2022). I4.0 has evolved from being used only at the production level to the supply chain, the way corporations contact customers and potential customers, workers and consumers (Liu and Zhao, 2022). It allows the development of new products and services, making it possible to add new digital features to existing ones (Liu and Zhao, 2022) and expanding the channels used to interact with actual or potential customers and providers. It also enables rationalizing and automating processes in such a way that costs are reduced, productivity is improved (Dalenogare et al., 2018) and supply chain performance is enhanced (Qader et al., 2022).

Lee argued that websites needed to move away from a search model, where users have to go digging for answers themselves. “Chatbots,” before ChatGPT revolutionized the world of AI, was a bit of a dirty word. To many consumers, a chatbot was a small box in the corner of screen, where a cheery automated program would offer to provide help–but then struggle to understand queries and deliver the right information. The first-level data flow diagram decomposition of these above business process operations is shown in Fig. The first-level data flow diagram decomposition of these business process operations is shown in Fig. IAssist chatbot assists employees with access to frequently asked questions (FAQ) through cognitive or intelligent search.

The bot will also likely give a “yes” or “no” response including an explanation of any important details. This allows the user to make these appointments and even purchase insurance without the need of a human employee, which saves time for the client insurer. This is likely convenient for Allstate in that new insurance information can be updated across the entire business using a single platform. This also likely allows ABIE to retrieve all information necessary to provide answers from one location. CBT is a form of therapy that includes managing mental health by reframing negative thoughts into positive or less self-critical ones.

Someone dealing with stress in a family relationship, for example, might benefit from a reminder to meditate. Or apps that encourage forms of journaling might boost a user’s confidence by pointing when out where they make progress. But last February, all that fell apart, after a car accident left Ali hobbled by injury, from head to knee. This configuration specifies that the OpenAI’s text-davinci-003 model should be used as the main LLM. The .yml files are highly customizable, allowing you to define various types of guardrails, actions, and even connect to different LLM providers. Having taken a deep dive into the inner workings of the ReAct agent, I hope you feel more confident implementing it for your projects.

Babylon Health’s platform leverages an AI-powered chatbot to generate diagnoses based on user responses. Users can interact with the chatbot in the same way they would when talking to primary care providers or other health professionals. Feebi is an AI chatbot equipped with features to replace restaurants’ human customer service processes.

Sami Mahmal, data lead for Zurich Insurance, pointed to an instance in Indonesia where the firm used AI to save time for the customer. The authors acknowledge the support provided for the study by the Cape Peninsula University of Technology (CPUT), South Africa, and the University of Pretoria, South Africa. Table 12 provides an overview of the number of vulnerabilities and threats per STRIDE component based on our analysis. Figure 14 gives the second level of the WhatsApp data flow diagram decomposition of the above business operations. Doug Marquis joined Zywave in 2018 as chief technology officer, leading the company’s R&D functions.

• The main arguments for its significance center on the relevance of its cognitive and relational dimensions defined in Glikson and Woolley (2020).
• The excitement (and sometimes amusement) from ChatGPT’s responses also trigger more creative applications.
• Statton explains that RAG overcomes the limitations of traditional language models by incorporating a retrieval step that allows the AI system to access relevant information from a knowledge base before generating a response.

So, we need to prepare for that very different demographic which will require different insurance solutions. And together with the changing demographic, young people want to buy things differently. I think common to all insurance we have is the impending global recession that ChatGPT App everyone is talking about. In order to get past that, I think insurance needs to adopt a prudent approach towards investment and risk-taking, and this is what I’ve always been doing. Fundamentally, we believe that if we take a long-term view, the future looks bright for us.

Progress Software does not list any case study showing an insurance provider’s success with the software. Instead of just analyzing existing data and performing tasks, ChatGPT can create new content based on its training. Beyond that, half of survey respondents from the insurance industry said they use AI and ML to predict business performance, and 46% use it to manage risk. Now more than ever, it’s important for insurance leaders to make wise decisions about where to spend their budget. Insurance leaders use AI Opportunity Landscapes to discover what their competitors are doing with AI.

ABIe (pronounced “Abbie”) was developed to assist Allstate agents seeking information on  Allstate Business Insurance (ABI) commercial insurance products. The VC firm has invested in companies such as Snapsheet, a smartphone application that reportedly allows users to receive auto repair bids from local body shops within 24 hours. Snapsheet’s president CJ Przybyl has stated that AI and machine learning are used to support the company’s data analysis process. Prioritizing the right claim at the right time is important to successful claims management. However, claims managers often face challenges identifying and assessing severe claims due to large caseloads and vast amounts of data to manage. AI-powered solutions alleviate this burden by learning from millions of claims and constantly evaluating their severity and risk.

In addition to drones, robotic technologies are also being used for risk assessment and inspections. For example, robotic crawlers can inspect pipelines, industrial facilities, and other infrastructure, providing detailed data on potential risks and vulnerabilities. These robots can operate in hazardous environments, reducing the risk to human inspectors and improving the accuracy of assessments.

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. This type of chatbot would require a machine learning model that has been trained to recognize topics and phrases from multiple “sets” of words at a time. These sets are categorized ChatGPT as languages, and the software can correlate phrases from either language to data points that may be most helpful. Avaamo.ai has two main insurance chatbot products that can help both insurance customers and employees. The customer-facing chatbots help the user decide on the right policy for them and with moving forward to purchasing it.

Natural language-processing capabilities and an understanding of customer data mean AI could become an excellent solution to provide a more personalized, efficient and convenient user experience in banking and financial services. Ethics has never been a strong suit of Silicon Valley, to put the matter mildly, but, in the case of A.I., the ethical questions will affect the development of the technology. Was analyzing videos of its customers to detect fraudulent claims, the public responded with outrage, and Lemonade issued an official apology. If users fear artificial intelligence as a force for dehumanization, they’ll be far less likely to engage with it and accept it. Progressive Insurance is reportedly leveraging machine learning algorithms for predictive analytics based on data collected from client drivers.