Ultimate Guide to Conversational AI For Insurance

insurance chatbot use cases

This makes sure no customer is left unanswered and allows the customer to connect to a live agent if required, keeping customers satisfied at all times. An insurance chatbot is an AI-powered virtual assistant solution designed to help ease communication between insurance companies and their customers. It uses artificial intelligence (AI) and machine learning (ML) technologies to automate a variety of processes and steps that customer support people often do in the industry. 80% of inbound customer queries are routine and insurance chatbots can easily resolve these queries while redirecting the remaining 20% to human agents.


Particularly with the development of artificial intelligence (AI), many theories have been made concerning how quickly robots would take over the workforce. The user interface (UI) of your chatbots will metadialog.com determine how successfully they’re used. Features such as buttons, quick replies, and multiple-choice selections can reduce the amount of text presented to users and speed up their interactions.

Chatbot for Insurance: Industry Innovation

If you’d like to develop a chatbot for insurance, drop us a note on or just ‘Get In Touch’ with us. We’d be happy to chat, learn more about your use case and build an interactive chatbot that can assist you in increasing conversion and customer retention with the power of conversational AI. The end goal for every insurance chatbot is to make every interaction as human, as personalized, and as native to the parent site, as possible. Research shows that if a customer query is not responded to within 5 minutes, the odds of converting them into a lead decreases by over 400%. In such situations, the presence of an insurance chatbot not just increases the chance of lead conversion, but also gratifies the user with an instant reply.

insurance chatbot use cases

Consumers are expecting more and more talks about their purchases to take place online, whenever they want, as they have become spoiled by the ease that e-commerce offers. This highlights the function of chatbots in a market crowded with companies competing to provide the best customer support. Almost from the moment, chatbots hit the market, customer-facing industries leaped on board.

Insurance chatbot use cases

They keep learning from information gathered, understand patterns of behavior and have a broader range of decision-making skills. Most insurance carriers have large contact centers with hundreds of customer support employees. However, the massive amount of queries coming in is difficult to handle for even such a large call center. Tie this in with the fact that the average response time is directly related to customer satisfaction. Chatbots, initially can provide the first level of support and allow human agents to focus on value-added tasks. In any industry that has high levels of interactions and transactions across the stakeholder ecosystem, it’s easy to argue the case for more automated and frictionless engagement.

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When a customer is attempting to purchase a specific service or product, there is a brief moment to compare other available products. It is critical to note that suggesting relevant products is essential for effective cross comparing. Chatbots can leverage previously acquired information to predict and recommend insurance policies a customer is most likely to buy. The chatbot can then create a small window of opportunity through conversation to cross-sell and up-sell more products. Since Chatbots store customer data, it is convenient to use data based on a customer’s intent and previously bought products with a higher probability of sale. For processing claims, a chatbot can collect the relevant data, from asking for necessary documents to requesting supporting images or videos that meet requirements.

7 Real-Time Service

The necessity for physical and eligibility verification varies depending on the type of insurance and the insured property or entity. A chatbot can assist in this process by asking the policyholder to provide pictures or videos of any damage (such as from a car accident). The bot can either send the information to a human agent for inspection or utilize AI/ML image recognition technology to assess the damage. Chatbots can gather information about a potential customer’s financial status, properties, vehicles, health, and other relevant data to provide personalized quotes and insurance advice.

insurance chatbot use cases

This lack of understanding often leads to a lack of investment in chatbot development. You can use this feedback to improve the client experience and make changes to products and services. Chatbots can educate clients about insurance products and insurance services. In addition, chatbots can handle simple tasks such as providing quotes or making policy changes. AI-powered recommendation engines can identify the right services and products for agents to cross or up-sell, and the exact moment during a conversation or the customer journey that a policyholder is likely to purchase.

Redefining CX In Insurance Sector With Conversational AI

Further questions can identify which stage of the customer journey the potential lead is at and which products or services they may be interested in. By accessing databases concerning available doctors, nurses, or other practitioners, as well as available appointment times, they can offer appointments to healthcare users and book them in. This automates the process and saves both patients and healthcare staff valuable time. Customers with questions about account management, payment options, postage costs, returns processes, or anything else can turn to chatbots to quickly find the answers they’re looking for. Providing visitors with a quick way to obtain this information can help reduce bounce and cart abandonment rates.

insurance chatbot use cases

Some questions in the study inquired specifically about healthcare and health insurance. The lack of post-sales service and support happens to be one of the major reasons why agents decide to end their relationship with the insurance provider. Beyond providing them with education about the products, they also need to be supported on aspects pertaining to commissions, payment terms and policies. Most large insurance providers today are exploring their digital transformation journeys. One of the big initiatives we find among insurance firms is the drive towards modernising their customer experience journey. With the multitude of channels available, resources employed to oversee the channels, and contact centres to manage everything, the cost of servicing can mount quite quickly, compromising ROI.


This will give the insurance companies a complete background of the customer and make changes to fit the customers’ requirements. The most successful insurance chatbots will be the ones that will drive a conversation perfectly mimicking a human agent. Almost every marketing guru will agree that it is treating customers with the respect they need and that’s the reason customer-centric strategies are now taking center stage.

How technology will impact the insurance industry?

An insurer can provide more customized premium offerings to customers if in fact they have a holistic view of the pertinent data. Pricing strategies, claim fraud mitigation, lead generation, and customer satisfaction are a few of the areas where data analytics can provide competitive advantages.

The insurance industry is mostly identified with heavy paperwork, complexities, and legacy processes. Today, the offers and products provided by an insurance company are not enough to set it apart. Customer experience is the only brand differentiator that should not be ignored and support centers are the solution to meet the changing customer requirements. However, such customer support centers are expensive as well as obsolete because the customers find it difficult to access the support assistants through calls and messages. In addition, large customer service is required for a large number of customer requests, ensuring 24/7, individual interaction across multiple digital channels and different languages.

Insurance Chatbot Use Cases Along the Customer Journey

NLP models can analyse these interactions to develop new marketing campaigns. These along with voice recognition techniques can also detect emotions in customer speech to improve personalisation. Brokers sell insurance policies on behalf of one or multiple insurance companies. Agents receive repetitive questions and requests, and bots can cover these issues, being automated and suggesting the most appropriate responses based on the information a customer has provided. They can have a complex architecture as they have to comprehend different scenarios, demographics, uses, etc., to provide the most suitable policies and guide their customers through the purchase process.

  • Robo advisory solutions which analyse market movements to help investors forecast accurately are also becoming popular.
  • Marketing automation is just one of the ways that chatbots can assist retailers.
  • Chatbots use prompts to engage visitors to a carrier’s website, social media, and other online touchpoints.
  • The support team was logically enthusiastic and committed to the HAL project.
  • Using chatbots ensures that the information provided is up-to-date and consistent with the insurer’s policies and standards.
  • In the case of state employees there can even be additional nuances that are different to that of the private sector.

Research suggests that 73% of customers are more likely to respond over live chat than e-mail, and 56% of users are more likely to contact the business through a message rather than a call. This is because people are used to seeing websites as a static medium, so any kind of engagement happening on the medium makes for excellent customer experience. That apart, they can also encourage customers to drop positive reviews and collect their feedback. There are times when you want the content on your page to prompt the user to take the next step.

Top Benefits of Chatbots for Businesses & Customers

“Automating estimating” using AI vision is something they follow which means extracting 75-year-old content from the Mitchell database and integrating it with their conversational AI bot. New customers who are digital natives and have high expectations for how a business handles them have emerged due to generational shifts. Given that one-third of customers said they would think about switching firms after just one instance of subpar customer service, these expectations shouldn’t be taken lightly. Creating a chatbot that provides the kind of benefits that insurance businesses need requires a specific set of skills.

How chatbots impact insurance industry?

Cost Reduction – By using a chatbot, an insurance company can significantly reduce its customer support costs. Chatbots provide instant resolution and fast response to a major volume of customer queries that would otherwise require a large amount of customer support staff.

How AI can be used in insurance?

Narrow-AI is already being used in many industries. In insurance, it has three main functions: First, it can automate repetitive knowledge tasks (e.g., classify submissions and claims) Second, it can generate insights from large complex data sets to augment decision making (e.g., portfolio steering, risk assessment)

Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing

symbolic artificial intelligence

The Disease Ontology is an example of a medical ontology currently being used. At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to accelerate the development of AI applications and research. In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were selling expert system shells, training, and consulting to corporations. For now, neuro-symbolic AI combines the best of both worlds in innovative ways by enabling systems to have both visual perception and logical reasoning. And, who knows, maybe this avenue of research might one day bring us closer to a form of intelligence that seems more like our own.

symbolic artificial intelligence

This makes them inherently unwieldy and uninterpretable, and in many ways unsuited for “augmented cognition” in conjunction with humans. Hybrids that allow us to connect the learning prowess of deep learning, with the explicit, semantic richness of symbols, could be transformative. In 2016, Yann LeCun, Bengio, and Hinton wrote a manifesto for deep learning in one of science’s most important journals, Nature.20 It closed with a direct attack on symbol manipulation, calling not for metadialog.com reconciliation but for outright replacement. Later, Hinton told a gathering of European Union leaders that investing any further money in symbol-manipulating approaches was “a huge mistake,” likening it to investing in internal combustion engines in the era of electric cars. Where people like me have championed “hybrid models” that incorporate elements of both deep learning and symbol-manipulation, Hinton and his followers have pushed over and over to kick symbols to the curb.

Computer Science > Artificial Intelligence

The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. In the history of the quest for human-level artificial intelligence, a number of rival paradigms have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks.

symbolic artificial intelligence

Similarly, scientists have long anticipated the potential for symbolic AI systems to achieve human-style comprehension. And we’re just hitting the point where our neural networks are powerful enough to make it happen. We’re working on new AI methods that combine neural networks, which extract statistical structures from raw data files – context about image and sound files, for example – with symbolic representations of problems and logic.

The benefits and limits of symbolic AI

Knowledge representation is used in a variety of applications, including expert systems and decision support systems. Symbolic AI, also known as rule-based AI or classical AI, uses a symbolic representation of knowledge, such as logic or ontologies, to perform reasoning tasks. Symbolic AI relies on explicit rules and algorithms to make decisions and solve problems, and humans can easily understand and explain their reasoning. When considering how people think and reason, it becomes clear that symbols are a crucial component of communication, which contributes to their intelligence. Researchers tried to simulate symbols into robots to make them operate similarly to humans.

symbolic artificial intelligence

In particular, a human observer would not be able to readily recognize what is being represented. In a deep learning context, these distributed representations are called embeddings, are learned during training, and are thus an explicit part of the architecture of the deep learning system. On a fundamental level, these two types of representing information are very different indeed.

Computer Science

Symbols can represent abstract concepts (bank transaction) or things that don’t physically exist (web page, blog post, etc.). Symbols can be organized into hierarchies (a car is made of doors, windows, tires, seats, etc.). They can also be used to describe other symbols (a cat with fluffy ears, a red carpet, etc.).


In order to make machine think and perform like human beings, researchers have tried to include symbols in them. Learning games involving only the physical world can easily be run in simulation, with accelerated time, and this is already done to some extent by the AI community nowadays. While this may be unnerving to some, it must be remembered that symbolic AI still only works with numbers, just in a different way. It helps AI recognize objects in videos, analyze their movement, and reason about their behaviors and not only to understand have casual relationships but applied common sense to solve problems. Not only these industries but also Intel, Google, Facebook, and Microsoft, and researchers like Josh Tenenbaum, Anima Anandkumar, and Yejin Choi, are starting to apply this technique in 2022.

Natural Language Processing

LISP is the second oldest programming language after FORTRAN and was created in 1958 by John McCarthy. LISP provided the first read-eval-print loop to support rapid program development. Program tracing, stepping, and breakpoints were also provided, along with the ability to change values or functions and continue from breakpoints or errors. It had the first self-hosting compiler, meaning that the compiler itself was originally written in LISP and then ran interpretively to compile the compiler code.

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An early boom, with early successes such as the Logic Theorist and Samuel’s Checker’s Playing Program led to unrealistic expectations and promises and was followed by the First AI Winter as funding dried up. A second boom (1969–1986) occurred with the rise of expert systems, their promise of capturing corporate expertise, and an enthusiastic corporate embrace. Neuro-symbolic systems have already demonstrated the capability to outperform state-of-the-art deep learning models in domains such as image and video reasoning. While neural networks are the most popular form of AI that has been able to accomplish it, symbolic AI once played a crucial role in doing so. Language is a type of data that relies on statistical pattern matching at the lowest levels but quickly requires logical reasoning at higher levels. Pushing performance for NLP systems will likely be akin to augmenting deep neural networks with logical reasoning capabilities.

Cultivating Joy in Science

Advantages of multi-agent systems include the ability to divide work among the agents and to increase fault tolerance when agents are lost. Research problems include how agents reach consensus, distributed problem solving, multi-agent learning, multi-agent planning, and distributed constraint optimization. Constraint solvers perform a more limited kind of inference than first-order logic. They can simplify sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, along with solving other kinds of puzzle problems, such as Wordle, Sudoku, cryptarithmetic problems, and so on.

What is symbolic AI in artificial intelligence?

What is Symbolic AI? Symbolic AI is an approach that trains Artificial Intelligence (AI) the same way human brain learns. It learns to understand the world by forming internal symbolic representations of its “world”. Symbols play a vital role in the human thought and reasoning process.

What is symbolic AI in NLP?

Symbolic logic

Commonly used for NLP and natural language understanding (NLU), symbolic AI then leverages the knowledge graph, to understand the meaning of words in context and follows IF-THEN logic structure; when an IF linguistic condition is met, a THEN output is generated.

Cognitive Automation Services and Cognitive Process Automation

cognitive automation solutions

AI-powered automation can also help to reduce the time and effort required to complete tasks, as well as increase accuracy and reduce errors. The value of intelligent automation in the world today, across industries, is unmistakable. With the automation of repetitive tasks through IA, businesses can reduce their costs as well as establish more consistency within their workflows. The COVID-19 pandemic has only expedited digital transformation efforts, fueling more investment within infrastructure to support automation. Individuals focused on low-level work will be reallocated to implement and scale these solutions as well as other higher-level tasks.

cognitive automation solutions

Middle managers will need to shift their focus on the more human elements of their job to sustain motivation within the workforce. Automation will expose skills gaps within the workforce, and employees will need to adapt to their continuously changing work environments. Middle management can also support these transitions in a way that mitigates anxiety to ensure that employees remain resilient through these periods of change. Intelligent automation is undoubtedly the future of work, and companies that forgo adoption will find it difficult to remain competitive in their respective markets. Intelligent process automation is the way artificial intelligence technologies, machine learning, cognitive automation, and computer vision are applied to benefit in operational business processes.

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It now has a new set of capabilities above RPA, thanks to the addition of AI and ML. Some of the capabilities of cognitive automation include self-healing and rapid triaging. The issues faced by Postnord were addressed, and to some extent, reduced, by Digitate‘s ignio AIOps Cognitive automation solution. Deliveries that are delayed are the worst thing that can happen to a logistics operations unit.

  • As ML technology continues to evolve, businesses should consider incorporating it into their RPA and cognitive automation strategies.
  • AI-powered cognitive automation can help to automate complex tasks that would otherwise require human intelligence, such as natural language processing, image recognition, and decision-making.
  • Cognitive automation refers to the head work or extracting information from various unstructured sources.
  • Just as machines have revolutionized manufacturing, so will Cognitive RPA in business processes.
  • Our highly accurate computer vision algorithms and decision engines make it possible to avoid human errors and to resolve tasks with near-human precision.
  • While there is evidence that these algorithms benefit from human annotations, efforts are being made to determine whether there are more effective ways to learn from observations of human activity.

Its ability to address tedious jobs for long durations helps increase staff productivity, reduce costs and lessen employer attrition. Our consultants identify candidate tasks / processes for automation and build proof of concepts based on a prioritization of business challenges and value. It enables chipmakers to address market demand for rugged, high-performance products, while rationalizing production costs.

Areas in which CPA is already playing a big role in helping clients meet their operation goals include –

However, if you are impressed by them and implement them in your business, first, you should know the differences between cognitive automation and RPA. The cognitive solution can tackle it independently if it’s a software problem. If not, it alerts a human to address the mechanical problem as soon as possible to minimize downtime.

What is an example of intelligent automation solution?

What is an example of an intelligent automation solution that makes use of artificial intelligence? signing-in to various desktop applications. filling out forms with basic contact information. copying text from a web browser.

Cognitive automation can also help businesses stay ahead of the competition by providing real-time insights into market trends. By analyzing data from various sources, businesses can gain a better understanding of the market and make more informed decisions. Cognitive automation can also help businesses stay ahead of the competition by providing insights into customer behavior.

Why Outsource Cognitive Computing Services to Getsmartcoders?

Provide managed services for clients’ Applications/Business processes and own maintenance. One of the most important documents in loan processing – the closing disclosure – has become extremely difficult to extract information from. It contains critical information that is necessary for post-close audits and validating loan metadialog.com information for accuracy. It is simply the bringing-together of fully baked solutions into a single platform. Until now the “What” and “How” parts of the RPA and Cognitive Automation are described. A task should be all about two things “Thinking” and “Doing,” but RPA is all about doing, it lacks the thinking part in itself.

  • Examples of everyday routine process automation are all around us as it’s depicted here.
  • Visual patient condition monitoring solutions make remote care easier and far more scalable.
  • We leverage configurable business-focused frameworks and in-house accelerators to speed up solution implementation.
  • We use engaging mobile apps integrated to CRM giving a hyper-personalized experience for loyal customers with schemes and offers, menu recommendations, and combination of beverages for multi-course meal.
  • The system uses machine learning to monitor and learn how the human employee validates the customer’s identity.
  • Asurion was able to streamline this process with the aid of ServiceNow‘s solution.

When it comes to FNOL, there is a high variability in data formats and a high rate of exceptions. Customers submit claims using various templates, can make mistakes, and attach unstructured data in the form of images and videos. Cognitive automation can optimize the majority of FNOL-related tasks, making a prime use case for RPA in insurance. Cognitive automation is an invaluable tool for businesses looking to stay ahead of the competition in a rapidly changing world. By automating processes, gaining insights into customer behavior, and providing predictive analytics, businesses can stay ahead of the competition and remain competitive.

Empowering Users

Intelligent Automation as comprising technology in the field of RPA (Robotic Process Automation) and AI (Artificial Intelligence) is aimed at enabling business processes automation and digital transformation performance. Zuci has been at the forefront of solving business problems using AI, ML, and computer vision technologies with Robotic Process Automation. We transform unstructured tasks into rule-based and structured tasks which enables end-to-end enterprise automation, thanks to our cognitive automation expertise. Typically, organizations have the most success with cognitive automation when they start with rule-based RPA first. After realizing quick wins with rule-based RPA and building momentum, the scope of automation possibilities can be broadened by introducing cognitive technologies. What’s important, rule-based RPA helps with process standardization, which is often critical to the integration of AI in the workplace and in the corporate workflow.

Is AI a cognitive technology?

Cognitive technologies, or 'thinking' technologies, fall within a broad category that includes algorithms, robotic process automation, machine learning, natural language processing and natural language generation, reaching into the realm of artificial intelligence (AI).

With RPA adoption at an all-time high (and not even close to hitting a plateau), now is the time business leaders are looking to further automation initiatives. Cognitive automation technology works in the realm of human reasoning, judgement, and natural language to provide intelligent data integration by creating an understanding of the context of data. UiPath is all about taking bigger chances and expanding the industry’s horizons.

The 3 components of intelligent automation

Using AI/ML, cognitive automation solutions can think like a human to resolve issues and perform tasks. It takes unstructured data and builds relationships to create tags, annotations, and other metadata. It seeks to find similarities between items that pertain to specific business processes such as purchase order numbers, invoices, shipping addresses, liabilities, and assets.

  • Cognitive automation techniques can also be used to streamline commercial mortgage processing.
  • Automated systems can work well if the decisions are made according to a “if/then” logic without requiring any human judgment in between.
  • Whether it’s more accurate troubleshooting of customer problems, or better overall customer service, cognitive automation helps businesses better meet the needs of their customers in real time through a more personalized experience.
  • We help organisations integrate both modern and legacy applications through the use of our high speed, robust, advanced integration technologies.
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  • RPA is a form of automation that allows computers to complete tasks that are normally done by humans.

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Karev said it’s important to develop a clear ownership strategy with various stakeholders agreeing on the project goals and tactics. For example, if there is a new business opportunity on the table, both the marketing and operations teams should align on its scope. They should also agree on whether the cognitive automation tool should empower agents to focus more on proactively upselling or speeding up average handling time. Meanwhile, you are still doing the work, supported by countless tools and solutions, to make business-critical decisions.

cognitive automation solutions

RPA bots can successfully retrieve information from disparate sources for further human-led KYC analysis. In this case, cognitive automation takes this process a step further, relieving humans from analyzing this type of data. Similar to the aforementioned AML transaction monitoring, ML-powered bots can judge situations based on the context and real-time analysis of external sources like mass media. Cognitive automation is a form of artificial intelligence that enables computers to replicate the human brain’s ability to understand, learn, and make decisions. This technology can be used for tasks such as natural language processing, image recognition, and data analytics.

Key Benefits – Cognitive Automation

Pedestrian and traffic monitoring automation, AI-based public threats analysis, proactive forensics can make every city a safer place. With our visual analysis modules and cognitive decision-making algorithms, the process can be fully automated. Our highly accurate computer vision algorithms and decision engines make it possible to avoid human errors and to resolve tasks with near-human precision. With that, healthcare pipelines involving visual analysis can perform faster, more accurately, and in a fully automated manner. AIHunters successfully resolves these challenges with its advanced cognitive computing-based video processing algorithms to automate the most routine parts of editing and post-production.

cognitive automation solutions

It just offloads the mundane, middle part of the process, like a highly trained assistant. The technology acts as a “virtual worker” that comes pre-trained and can adapt to the unique habits of an individual user. Our solutions have inbuilt components which ensure that concerned staff is alerted via email or other real-time notifications when the system reports a confidence level lower than the benchmark. Here we test our solution with random sample data and evaluate the model’s accuracy. All cycles for improvement are performed to adjust the solution exactly as per requirements.


State-of-the-art technology infrastructure for end-to-end marketing services improved customer satisfaction score by 25% at a semiconductor chip manufacturing company. The cognitive automation solution is pre-trained and configured for multiple BFSI use cases. Longer implementation cycles further add to the complexity in incorporating evolving business regulations into operations, leading to diminishing returns, increased costs, and transformation hiccups. With the ever-changing demands in the marketplace, businesses must take aggressive steps to meet the needs of their customers in real time, and keep up with their fast-paced competitors.

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What are cognitive systems in AI?

The term cognitive computing is typically used to describe AI systems that simulate human thought. Human cognition involves real-time analysis of the real-world environment, context, intent and many other variables that inform a person's ability to solve problems.