Its for Real: Generative AI Takes Hold in Insurance Distribution Bain & Company

With the ultra-low power technology from E ink and Lenovo’s system design, the color-changing top cover won’t impact the battery life – even when the system is powered off, the top cover can still keep changing. Lenovo will highlight four design schemes at CES 2024, including two colorful schemes, dynamic clock and multi system interaction, that offer different and unique customer preferences. Lenovo’s AI-ready mobile workstations are a game-changer for user productivity across a wide range of industries. With their capability to turbocharge workflows, from data analysis to creative processes, these mobile workstations provide the accelerated power needed for even the most sophisticated workloads.

For now, these tend to be human-in-the-loop processes — with potential to fully automate. This plan should address all four dimensions involved in any large-scale, analytics-based initiative—everything from data to people to culture (Exhibit 2). The plan should outline a road map of AI-based pilots and POCs and detail which parts of the organization will require investments in skill building or focused change management.

In fact, Accenture research ‘Why AI in Insurance Claims and Underwriting’ found that up to 40% of underwriters’ time is spent on non-core and administrative activities — an annual efficiency loss of between $17 billion and $32 billion. More than half (60%) of the underwriters surveyed believe that improvements could be made to the quality of their organizations’ processes and tools. The age-old insurance underwriting model worked well for a traditional environment of slower and more predictable change, with similar processes and risk evaluation methods across insurers. Whatever industry you’re in, we have the tools you need to take your business to the next level. However, companies that use AI to automate time-consuming, mundane tasks will get ahead faster. So now is the time to explore how AI can have a positive effect on the future of your business.

The key levers of change management—such as solution codesign, cascaded training, and the systematic tracking of adoption progress—play an important part in this. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting.

Managing the risks

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources. The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Karim Haji, Global Head of Financial Services, outlines why it’s such an exciting time for the financial services industry. As a balance to AI’s huge potential, KPMG research reveals that CEOs are acutely aware of the hurdles. Ethical issues around AI decision-making and the absence of robust regulation are the most prominent concerns. KPMG’s 2023 Insurance CEO Outlook highlights that 52 percent of CEOs see these as highly challenging. And the tech report reveals that nearly two-thirds (64 percent) of respondents say that complex regulatory and tax developments have to some/greater extent made them feel less confident about investing in new technologies.

The pace of change will also accelerate as brokers, consumers, financial intermediaries, insurers, and suppliers become more adept at using advanced technologies to enhance decision making and productivity, lower costs, and optimize the customer experience. KPMG in Israel assisted a large insurance company to develop a customer contact solution. By utilizing a variety of AI techniques to reduce the number of calls from customers, the organization aims to improve customer satisfaction and increase the efficiency of agents.

To determine how likely it is a prospective customer will file a claim, insurance companies run risk assessments on them. By understanding someone’s potential risk profile, insurance companies can make more informed decisions about whether to offer someone coverage and at what price. Indeed, MetLife’s AI excels in detecting customer emotions and frustrations during calls. Such an approach is particularly impactful in sensitive discussions about life insurance, where understanding and addressing buyer concerns promptly is vital.

Risks and human oversight

Join us on March 14 for a webcast featuring timely insurance industry updates from KPMG subject matter professionals. Ed Chanda and Kelly Combs discuss the potential for Gen AI in the insurance industry. We bring together passionate problem-solvers, Chat GPT innovative technologies, and full-service capabilities to create opportunity with every insight. Claims processing in 2030 remains a primary function of carriers, but more than half of claims activities have been replaced by automation.

By partnering with us, you can elevate your claim processing capabilities and bolster your defenses against fraud. Generative AI is not just the future – it’s a present opportunity to transform your business. After exploring various use cases of GAI in the insurance industry, let’s delve into four inspiring success stories from global companies.

So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers. In DeepLearning.AI’s AI For Good Specialization, meanwhile, you’ll build skills combining human and machine intelligence for positive real-world impact using AI in a beginner-friendly, three-course program. In DeepLearning.AI’s AI For Everyone course, you’ll learn what AI can realistically do and not do, how to spot opportunities to apply AI to problems in your own organization, and what it feels like to build machine learning and data science projects.

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). The results of our survey reinforce the idea that GenAI is more than a “nice to have” for insurers; it’s a “must have” capability required for incumbent players to catalyze transformation efforts across the enterprise.

Using a using Natural Language Processing (NLP) and a classification algorithm, KPMG helped the client to analyze and then categorize calls to the support center. Overall, the analysis showed that many of the queries could be handled more effectively through a self-service solution. KPMG professionals are working closely with the insurance business to consider how an AI-based solution will enable customers to simply ask a virtual assistant question like “what is my life insurance coverage? This provides a cost-effective way to answer queries the first time, while reducing call volumes and improving customer satisfaction.

Lifestyle patterns and medical costs are rapidly changing risk exposure and indemnity costs of disability, workers’ compensation and retirement. AI and gen AI will continue to have a significant impact on many organizations, whether they are providers of AI models or users of AI systems. Despite the rapidly changing regulatory landscape, which is not yet aligned across geographies and sectors and may feel unpredictable, there are tangible benefits for organizations that improve how they provide and use AI now. For instance, it can automate the generation of policy and claim documents upon customer request. This automation eliminates the need for human staff to manually process these requests, significantly reducing wait times and improving efficiency. Customers receive the documents they need promptly, precisely when they need them.

Their strategy involves generating an immense 1.5 to 2 petabytes of information. The records will encompass AI-generated medical histories and healthcare claims. The aim is to refine and train artificial intelligence algorithms on these extensive datasets, while also addressing privacy concerns around personal details. At Allianz Commercial, Generative AI also plays a multifaceted role in enhancing customer service and operational efficiency. They use intelligent assistants to answer user queries about risk appetite and underwriting. These bots are available 24/7, operate in multiple languages, and function across various channels.

The Intel Core Ultra processors not only enhance the performance of AI features but also contribute to power efficiency. For example, the processors facilitate improved collaboration experiences such as AI-powered video conferencing, which includes background blur, face framing, and eye contact correction capabilities. The NPU effectively offloads these tasks from the CPU or GPU, resulting in up to a 40% reduction in power consumption5. Consequently, users can enjoy longer battery life reducing the need for frequent recharging.

Policy & Public Interest

Convolutional neural networks and other deep learning technologies currently used primarily for image, voice, and unstructured text processing will evolve to be applied in a wide variety of applications. In 2030, underwriting as we know it today ceases to exist for most personal and small-business products across life and property and casualty insurance. The process of underwriting is reduced to a few seconds as the majority of underwriting is automated and supported by a combination of machine and deep learning models built within the technology stack. These models are powered by internal data as well as a broad set of external data accessed through application programming interfaces and outside data and analytics providers. Information collected from devices provided by mainline carriers, reinsurers, product manufacturers, and product distributors is aggregated in a variety of data repositories and data streams.

As much as technologies reshape the world, people are the true drivers of change. Creating a culture of innovation is not just equipping teams with the right tools but also inspiring them to think creatively about how to use them. As insurance companies look at how best to leverage new technologies, part of the focus should be on talent management — developing a team with the right technical capabilities while empowering existing colleagues to upskill and help the workforce adapt. Building on the insights from AI explorations, carriers must decide how to use technology to support their business strategy. The senior leadership team’s long-term strategic plan will require a multiyear transformation that touches operations, talent, and technology.

Besides earning lower incomes than other age groups, young Americans buy a disproportionate share of products and services that have soared in price. For instance, they devote nearly 20% of their income to rent, averaged across the entire age group, compared to 7% for the average American, Moody’s data shows. Chatbots can also be used to identify potential upselling and cross-selling opportunities for voluntary benefits, improving overall sales and profitability. By integrating with health data providers (e.g., Fitbit) and behavioral data tools, this functionality can become even more personalized, tailored to the individual’s unique situation and their goals.

The solution offers a unique innovation providing a Hybrid experience between Windows and Android™ system. The ThinkBook 13x Gen 4 is a beautiful and powerful Intel Evo Edition laptop with fantastic battery life, a built-in Copilot key, and is Lenovo’s first carbon-neutral laptop for SMBs4. The ThinkBook 14 i Gen 6+5 is a powerful, versatile, and smart laptop with up to stunning 14.5-inch 3K display and includes a Graphics Extension (TGX) port that supports the new ThinkBook Graphics Extension (TGX)5 dock boosting AI computing power. Lenovo is also introducing its updated ThinkBook 16p Gen 5 combining power and elegance with support for the new Magic Bay Studio that incorporates a 4K camera6 and integrated speakers.

This success demonstrates the power of digital assistants in providing personalized education and guidance to insurance customers. Traditional new business and renewal processes https://chat.openai.com/ can be time-consuming and result in missed opportunities. It’s helpful for sales and underwriting teams to have data-backed reference points when designing plans for clients.

This also links back to regulation as insurers with unstructured or fragmented data will face significant challenges in meeting new legislation and building trust in the market. The insurance value chain, from product development to claims management, is a complicated process. The complex nature of tasks like risk assessment and claims processing poses significant challenges for an insurance company. Generative Artificial Intelligence (AI) emerges as a promising solution, capable of not only streamlining operations but also innovating personalized services, despite its potential challenges in implementation.

The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use. You can foun additiona information about ai customer service and artificial intelligence and NLP. Digital solutions can make the high-stakes claims experience seamless, but industry data indicates a chasm between customer preferences and reality. By streamlining quoting, optimizing resources and automating manual tasks, AI can help increase group insurance sales, boost profitability and improve the customer experience.

There, Turing described a three-player game in which a human “interrogator” is asked to communicate via text with another human and a machine and judge who composed each response. If the interrogator cannot reliably identify the human, then Turing says the machine can be said to be intelligent [1]. Artificial general intelligence (AGI) refers to a theoretical state in which computer systems will be able to achieve or exceed human intelligence.

There are a lot of promising methods, including reinforcement and imitation learning, but future solutions will likely involve combinations of these methods, augmented by generative AI models. Rob Meikle, Executive CounselorRob Meikle is an Executive Consultant, Keynote Speaker, and Board Advisor to several emerging technology organizations. Meikle is the former CIO for the City of Toronto and the City of Brampton, Ontario, Canada, where he created high-performance teams to deliver innovative and transformational solutions.

AI is poised to revolutionize consumer experiences and reshape the narrative of insurance itself. Those who embrace this change will not only elevate the CX but also lead the industry into a new epoch. GAI’s implementation for threat review and pricing significantly enhances the accuracy and fairness of these processes. By integrating deep learning, the technology scrutinizes more than just basic demographics. It assesses complex patterns in behavior and lifestyle, creating a sophisticated profile for each user. Such a method identifies potential high-risk clients and rewards low-risk ones with better rates.

Embracing ecosystems and platforms can help insurers adapt to market changes and even reduce the risk market disruption. The interplay between traditional insurers and InsurTech firms is vital for fostering sector-wide innovation and expanding coverage to underserved segments. Collaboration could also help steer insurance toward a more inclusive, customer-centric, data-driven and tech-enabled future.

By identifying these resource efficiencies, insurers can write more business in less time while improving close ratios. The devil, as always, is in the detail, and there are a lot more of them than before for underwriters to analyze. Weather, health and other risk data has proliferated enormously, and forward-looking analytical models have become a gen ai in insurance critical tool in making sense of catastrophe probabilities. Geospatial data has the granularity to allow underwriters to differentiate between, for example, two properties on the same floodplain but with very different risk exposures. By analyzing patterns in claims data, Generative AI can detect anomalies or behaviors that deviate from the norm.

What generation is most affected by inflation?

Generative AI identifies nuanced preferences and behaviors of the insured from complex data. It predicts evolving market trends, aiding in strategic insurance product development. Tailoring coverage offerings becomes precise, addressing specific client needs effectively. This AI-driven approach spots emerging opportunities, sharpening insurers’ competitive edge. Generative AI has redefined insurance evaluations, marking a significant shift from traditional practices. By analyzing extensive datasets, including personal health records and financial backgrounds, AI systems offer a nuanced risk assessment.

These steps, among others, were critical to helping end users build trust in the tool. Gen ai refers to artificial intelligence systems that can generate brand new data, such as text, images,

audio or video. It works by learning patterns from training data, then leveraging that knowledge to create

new, original data.

Some of these companies have started to develop AI tools to support claims handlers, not replace them. They have achieved better data visualization, better access to and use of historical references, more timely alerts, and more confident data-driven recommendations. Together with image recognition and computer-vision technologies, GenAI can assess damages with unprecedented precision. Claims handlers can also use GenAI to scan external data sources, such as past court rulings, to gather evidence to support a possible negotiation. To drive better business outcomes, insurers must effectively integrate generative AI into their existing technology infrastructure and processes. Accordingly, insurers should improve existing processes and optimize them in parallel to achieve the maximum benefits of generative AI.

Such an enhancement is a key step in Helvetia’s strategy to improve digital communication and make access to product data more convenient. Such technologies revolutionize medical policy event management, making it faster, more accurate, and user-friendly. Furthermore, with Generative AI in health, insurers offer dynamic, client-centric help, boosting the overall experience. While generative AI is still in early days, insurers cannot afford to wait on the sidelines for another year.

• Highly dynamic, usage-based insurance (UBI) products proliferate and are tailored to the behavior of individual consumers.
• Claims handlers can also use GenAI to scan external data sources, such as past court rulings, to gather evidence to support a possible negotiation.
• It is important to note, that medical expenses also have been rising, which means higher costs for insurance companies when they cover injuries from accidents.
• These models are powered by internal data as well as a broad set of external data accessed through application programming interfaces and outside data and analytics providers.
• The use of generative AI in robotics has been a white-hot subject recently, as well.
• However, machines with only limited memory cannot form a complete understanding of the world because their recall of past events is limited and only used in a narrow band of time.

Generative AI is driving digital disruption in the insurance industry, as CEOs look to the new technology to deliver value through greater productivity, lower costs, and increasing growth opportunities. Learn more about the CEO’s unique role in insurance industry transformation in our report. Models such as GPT 3.5 and GPT 4 present opportunities to radically improve insurance operations. They have the potential to automate processes, enhance customer experiences and streamline claims management, ultimately driving efficiency and effectiveness across the industry.

These tools not only help insurers tap valuable existing sources of revenue but also reduce quote turnaround time, providing a better customer experience. I believe generative AI will transform every part of the insurance value chain, especially underwriting and claims. The ubiquitous spreadsheet will be around for a long time to come, but for underwriters at least its days are surely numbered. This abundance of data is impossible to input, assimilate and analyze manually, even with basic digital tools like spreadsheets.

Additionally, Gen AI is employed to summarize key exposures and generate content using cited sources and databases. For policyholders, this means premiums are no longer a one-size-fits-all solution but reflect their unique cases. Generative AI shifts the industry from generalized to individual-focused risk assessment. The targeted and unbiased approach is a testament to the customer-centricity in the sector. Our team diligently tests Gen AI systems for vulnerabilities to maintain compliance with industry standards.

In this article, we’ll explore the various ways in which group insurance providers can leverage AI to maximize sales and remain competitive. Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space. As the negative effects of AI become more widely known and publicized, public concerns increase. This, in turn, has led to public distrust of the companies creating or using AI.

The advanced algorithms of GenAI can quickly evaluate the likelihood of fraud and flag suspicious claims for further investigation, enabling insurers to allocate resources more effectively and reduce losses. GenAI will affect how work is organized and what skills are needed to enable desired outcomes. Specifically, CEOs have a unique opportunity to help their organizations understand that GenAI is not a job replacer; rather, it will affect the tasks and skills insurance organizations use to get the job done.

And with several tech giants intent upon disrupting the insurance market, it’s clear that traditional insurance products are struggling to keep pace with emerging customer lifestyles. Regulators review AI-enabled, machine learning–based models, a task that requires a transparent method for determining traceability of a score (similar to the rating factor derivations used today with regression-based coefficients). To verify that data usage is appropriate for marketing and underwriting, regulators assess a combination of model inputs.

Insurance executives need to be technically equipped in order to apply insights derived from the new accounting principles. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School. In addition, the AI could also explain the policy terms and conditions to the customer in simpler terms, enhancing transparency and trust.

This personalization can lead to more adequate coverage for the insured and better customer satisfaction. With the ‘Image Recognition’ feature, field teams can identify and count the quantity of any product on shelves, gather information on their prices and access share reports in each store, ensuring merchandising compliance. Leveraging AI, the tool also strategically optimizes commercial routes, maximizing efficiency in terms of distance, time, and sales opportunities.

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies.

• One of the prime use cases suggested by the MIT team is the ability to collate relevant information from these small, task-specific datasets.
• Our Reinventing Insurance podcast explores best practices for taking a CustomerFirst approach to innovation within Insurance.
• Though these terms might seem confusing, you likely already have a sense of what they mean.
• For now, these tend to be human-in-the-loop processes — with potential to fully automate.

Savings are a big driver, but its impact extends far beyond, offering opportunities for top-line growth as well. The democratization of AI enables employees to shift their focus to the most value-adding activities. One significant area of impact is sales and distribution, where administrative tasks can be reduced to free up more sales time.

In this article, we will explain 9 potential use cases of generative AI in insurance and talk about its own challenges that can be problematic in the insurance sector. To give users more control over the contacts an app can and cannot access, the permissions screen has two stages. One of the prime use cases suggested by the MIT team is the ability to collate relevant information from these small, task-specific datasets. Tasks include useful robot actions like pounding in a nail and flipping things with a spatula.

Enough information is known about individual behavior, with AI algorithms creating risk profiles, so that cycle times for completing the purchase of an auto, commercial, or life policy will be reduced to minutes or even seconds. Many life carriers are experimenting with simplified issue products, but most are restricted to only the healthiest applicants and are priced higher than a comparable fully underwritten product. As AI permeates life underwriting and carriers are able to identify risk in a much more granular and sophisticated way, we will see a new wave of mass-market instant issue products. With its ability to analyze data, generate content, and make predictions, generative AI offers a wide range of use cases for insurance companies. Insurers that embrace it stand to gain a competitive edge by leveraging its capabilities to meet the evolving needs of their customers and the industry.

More than 50% of their policies are now issued with zero human intervention, entirely digitally, and about 90% of renewals are also processed digitally. Generative AI, a subset of artificial intelligence, primarily utilizes Large Language Models (LLMs) and machine learning (ML) techniques. Although the foundations of AI were laid in the 1950s, modern Generative AI has evolved significantly from those early days. Machine learning, itself a subfield of AI, involves computers analyzing vast amounts of data to extract insights and make predictions.

A study of the consulting firm’s administrative employees demonstrates how they can use AI to reduce time spent on toil and increase time on joy-creating tasks. The research also explores the key factors that drive successful gen AI adoption; the main finding is that having a manager who is immersed in using AI will drive employee engagement with the technology. Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value. Natural language processing (NLP) can be used to train an AI system to read an RFP booklet, learn carrier-specific terms and abbreviations and generate a quote based on the information detected within the source document.

This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs. Machines that possess a “theory of mind” represent an early form of artificial general intelligence. In addition to being able to create representations of the world, machines of this type would also have an understanding of other entities that exist within the world. As for the precise meaning of “AI” itself, researchers don’t quite agree on how we would recognize “true” artificial general intelligence when it appears.

Given the frequency with which their developers toss around the phrase “general purpose humanoids,” more attention ought to be paid to the first bit. After decades of single-purpose systems, the jump to more generalized systems will be a big one. To learn more about Info-Tech’s divisions, visit McLean & Company for HR research and advisory services and SoftwareReviews for software-buying insights. AI has a range of applications with the potential to transform how we work and our daily lives. While many of these transformations are exciting, like self-driving cars, virtual assistants, or wearable devices in the healthcare industry, they also pose many challenges. Weedon also notes, “Labour shortages, particularly in skilled automotive repair and EV specialist roles, have further exacerbated the situation.

Our earlier research has shown that employees who enjoy their work are about 50% less likely to look for a new job. People work at work — and it is therefore critical for any effort to improve joy to be grounded in the day-to-day rhythms, routines, and tasks that employees spend their time on. Orrick said the first week of this year’s program included a training on prompt engineering, in addition to a day dedicated to seminars and ideas sessions around generative AI. There were explosive stock price increases at insurtechs that demonstrated higher-quality growth.