Generative AI in Insurance: Top 5 Use Cases

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The proof-of-concept product is an innovative solution that aligns with Lenovo’s commitment to implementing more sustainable practices. AccountsIQ, a Dublin-founded accounting technology company, has raised $65 million to build “the finance function of the future” for mid-sized companies. Bill Wong, AI Research FellowBill Wong is an AI Research Fellow at Info-Tech Research Group, where he leads research focused on generative AI, AI strategy, responsible AI, and AI regulations and legislation. He has led and guided hundreds of strategic AI initiatives worldwide, collaborating with C-level executives, data scientists, and data engineers.

The rapid evolution of the industry will be fueled by the extensive adoption and integration of automation, deep learning, and external data ecosystems. While no one can predict exactly what insurance might look like in 2030, carriers can take several steps now to prepare for change. While virtually all insurance companies are using AI today, its impact has fallen short of the transformative change that many had hoped for. Traditional AI has been restricted largely to an approach based on use cases, optimizing niches of existing operating models, rather than fundamentally transforming them. AI models can generate personalized insurance policies based on the specific needs and circumstances of each customer. Based on data about the customer, such as age, health history, location, and more, the AI system can generate a policy that fits those individual attributes, rather than providing a one-size-fits-all policy.

This feature enables the generation of itineraries for different field teams in a few minutes and it also summarizes the employee’s visits and performances in stores, saving workload for MotoTalk users. Motorola has introduced new AI features available on MotoTalk, a business productivity platform for PC and mobile devices that allows business customers to create and manage tasks and workdays for their teams. With the new features, field teams can use Image Recognition or Route Planning to optimize their daily activities.

Training and fine tuning generative models, particularly large ones, requires substantial computational resources. Smaller companies may struggle to implement generative AI tools due to the high costs involved. Ensuring the reliability and accuracy of the generated data or predictions is a significant challenge.

With AI’s potential exceedingly clear, it is easy to understand why companies across virtually every industry are turning to it. As insurers begin to adopt this technology, they must do so with a focus on manageable use cases. Our practical guide for insurance executives to help separate hype from reality, including Web3 insurance opportunities and risk considerations. © 2024 KPMG LLP, a Delaware limited liability partnership and a member firm of the KPMG global organization of independent member firms affiliated with KPMG International Limited, a private English company limited by guarantee. Financial services firms are performing better because of technology investments but now they need to fine-tune their digital transformation journeys.

It continuously learns from new datasets, enhancing suspicious activity identification and prevention strategies. Generative AI streamlines claim settlement procedures with impressive efficiency. It analyzes customer data, instantly identifying patterns indicative of legitimate or fraudulent cases. This rapid analysis reduces the time between submission and resolution, which is especially crucial in health-related situations.

In fact, KPMG LLP was the first of the Big Four firms to organize itself along the same industry lines as clients. KPMG’s multi-disciplinary approach and deep, practical industry knowledge help clients meet challenges and respond to opportunities. Member firms of the KPMG network of independent firms are affiliated with KPMG International. No member firm has any authority to obligate or bind KPMG International or any other member firm vis-à-vis third parties, nor does KPMG International have any such authority to obligate or bind any member firm. Innovation cannot be the domain of specialized teams alone — making it part of the organization ethos is key.

After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes. QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges.

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This lack of transparency and explainability can be a significant issue, particularly in a heavily regulated industry like insurance. For instance, after an accident, a customer may upload the details and pictures of the damaged vehicle. A generative model trained on similar data can evaluate the damage, estimate the repair costs, and hence help in determining the claim amount. The models can also generate appropriate responses to customer queries about the status or details of their claim, making communication more straightforward and efficient. Generative AI can generate examples of fraudulent and non-fraudulent claims which can be used to train machine learning models to detect fraud.

After exploring the capabilities of Generative AI and understanding how an enterprise LLM can be fine-tuned for a specific industry to deliver more efficient and accurate responses, let’s examine how Generative AI can be applied in the insurance industry. With the strategies and recommendations discussed, your company can navigate the technological advancements more effectively. The technology analyzes patterns and anomalies in the insured data, flagging potential scams. This AI application reduces fraudulent claim payouts, protecting businesses’ finances and assets.

Our diverse, global teams bring deep industry and functional expertise and a range of perspectives that question the status quo and spark change. BCG delivers solutions through leading-edge management consulting, technology and design, and corporate and digital ventures. We work in a uniquely collaborative model across the firm and throughout all levels of the client organization, fueled by the goal of helping our clients thrive and enabling them to make the world a better place. We asked Christopher Freese, the managing director and senior partner of BCG’s Insurance practice, to reflect on the potential of generative artificial intelligence to transform the insurance industry. One of the biggest challenges on the road to general-purpose systems is training. We have a solid grasp on best practices for training humans how to do different jobs.

Similarly, you can train Generative AI on customers’ policy preferences and claims history to make personalized insurance product recommendations. This can help insurers speed up the process of matching customers with the right insurance product. Although the tectonic shifts in the industry will be tech-focused, addressing them is not the domain of the IT team. Instead, board members and customer-experience teams should invest the time and resources to build a deep understanding of these AI-related technologies. Part of this effort will require exploring hypothesis-driven scenarios in order to understand and highlight where and when disruption might occur—and what it means for certain business lines.

This process helps spread risk and ensures that insurance companies remain financially stable, especially in the face of large-scale claim events. However, the cost of reinsurance has been rising, and this increase directly affects the premiums that consumers pay. Research by BCG shows that workers who spend too many hours on tasks they dislike (“toil”) are at risk for quitting, and employees who spend sufficient time on work that creates joy are less of a flight risk.

What is Gen AI?

Customer preparedness involves not only awareness of Generative AI’s capabilities but also trust in its ability to handle sensitive data and processes with accuracy and discretion. Surveys indicate mixed feelings; while some clients appreciate the increased efficiency and personalized services enabled by AI, others express concerns about privacy and the impersonal nature of automated interactions. By implementing Generative AI in their fraud prevention departments, insurance companies can significantly reduce the number of fraudulent claims paid out, boosting overall profitability. This, in turn, allows businesses to offer lower premiums to honest customers, creating a win-win situation for both insurers and insureds.

Generative AI offers staying power due to its robustness, ease of use, and low barrier to entry. In November 2022, OpenAI, an American artificial intelligence research lab, introduced GPT 3.5 and Chat GPT. ChatGPT rapidly reached 1 million users in five days, and 100 million users in less than two months. It is being used for search, customer insights and service, writing content, coding, video creation, and more. In March 2023, OpenAI released its next iteration GPT 4.0, a multimodal large language model that offers broader general knowledge and problem solving abilities.

As a result, the insurers can tailor policy pricing that reflects each applicant’s unique profile. While these are foundational steps, a thorough implementation will involve more complex strategies. Choosing a competent partner like Master of Code Global, known for its leadership in Generative AI development services, can significantly ease this process. At MOCG, we prioritize robust encryption and access controls for all AI-processed data in the insurance industry. While AI builds on existing data and analytics, it introduces new capability needs that will inform an evolving operating model.

Insurance executives must understand the factors that will contribute to this change and how AI will reshape claims, distribution, and underwriting and pricing. With this understanding, they can start to build the skills and talent, embrace the emerging technologies, and create the culture and perspective needed to be successful players in the insurance industry of the future. Lenovo also announced new ThinkBook products, ThinkCentre desktops, and accessories for the small and medium sized business (SMB) market, with innovative features, smart designs, and AI PC enhancements. The new ThinkBook Plus Gen 5 Hybrid is a flexible hybrid solution with a laptop base system and a tablet that can work independently or together and seamlessly switch between laptop and tablet.

Respondents’ expectations for gen AI’s impact remain as high as they were last year, with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead. While agents and brokers can’t offer personalized advice to thousands of individual insureds with varying needs and levels of knowledge, technology can. Digital assistants and chatbots can provide valuable guidance to plan members, answering questions, guiding them through the enrollment process and recommending new products and services based on their health and demographic information.

We help you discover AI’s potential at the intersection of strategy and technology, and embed AI in all you do. As the firm builds AI capabilities, it can focus on higher-value, more integrated, sophisticated solutions that redefine business processes and change the role of agents and employees. GenAI’s ability to recognize patterns and correlations in claims documentation, such as loss appraisers’ reports, can also help insurers identify areas of risk concentration and improve the feedback loop to underwriting and product design. Cumulative experience, employee tenure and engagement, and concurrent workload can influence claim handlers’ effectiveness, as well as the attitude they have if the value of the claim is disputed. Insurance CEOs can realize this value by taking targeted steps toward activation and scaling. Having a clear vision and strategy, setting up mature operating and governance models, and acquiring/developing the right talent at the right time will be key steps on their journey.

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For example, with Appian’s AI document extraction and classification, insurers can automate the manual work of analyzing policy documents. Or they can chat with AI Copilot to answer questions about a customer policy or claim. For more, check out our article on the 5 technologies improving fraud detection in insurance.

Artificial intelligence (AI) has the potential to revolutionize the group insurance industry, particularly during peak business periods, when human resources are stretched thin. Insurance companies can also use Generative AI to serve existing customers with personalized products and services. For example, you can develop a Conversational AI platform powered by Generative AI to answer specific, customer inquiries and questions about policy coverage and terms.

Waiting times for queries that require human input will likely be reduced and customer service agents can focus on customer queries that require human input. To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. The insurance industry has undergone significant transformation across its business operations in recent years. Traditional insurance business models have faced disruptive innovation, increased competition from agile InsurTechs and tech leaders offering insurance as a service, and changing customer expectations. Navigating these challenges while pursuing growth has been a key concern for insurance CEOs.

• January 8, 2024 – Today at CES® 2024, Lenovo unveiled a full lineup of more than 40 new devices and solutions powered by AI, furthering the company’s vision of AI for All.
• Appian partner EXL is actively working to explore the vast potential of generative AI and help insurers unlock the full power of this technology within the Appian Platform.
• As a result, the insurers can tailor policy pricing that reflects each applicant’s unique profile.
• The incentive to move fast is heightened by the fact that if the right governance and organizational models for AI are not built early, remediation may become necessary later due to regulatory changes, data breaches, or cybersecurity incidents.
• These information sources enable insurers to make ex ante decisions regarding underwriting and pricing, enabling proactive outreach with a bindable quote for a product bundle tailored to the buyer’s risk profile and coverage needs.

These automated processes not only save time but also reduce errors, providing a better customer experience. First, it is significantly more difficult for them to accurately assess and price risk. And second, with customers and brokers demanding less onerous application processes and quicker approval, carriers that still use traditional underwriting methods and outdated tools will find it increasingly difficult to compete. They also may struggle to attract and retain the scarce underwriters and actuaries who are vital to their business. The fact that the global average temperature for each of the previous 11 months was the highest in recorded history confirms that we’re in uncharted territory when it comes to climate change. New technology is empowering cyber-attackers in ways that we can currently only speculate.

He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. Also, these generated synthetic datasets can mimic the properties of original data without containing any personally identifiable information, thereby helping to maintain customer privacy. Intel-powered Lenovo Workstation portfolio and Anaconda Navigator streamline data science workflows. Most coverage of humanoid robotics has understandably focused on hardware design.

At least initially, a human remains in the loop to check the AI’s work, but the process is radically simplified. This significantly enhances many customers’ experiences, eliminating the need to endure lengthy processes with assessors. Generative AI can be used in creating chatbots that can generate human-like text, improving interaction with customers, and answering their queries in real-time. Implementing generative AI in insurance for customer service operations can increase customer satisfaction due to fast and 24/7 support, together with cost savings.

Despite some commonality in the guiding principles of AI, the implementation and exact wording vary by regulator and region. This makes it challenging for organizations to navigate regulations while planning long-term AI strategies. This article delves into the synergy between Generative AI and insurance, explaining how it can be effectively utilized to transform the industry. You will discover detailed use cases of Generative AI in insurance with examples.

Machines with self-awareness are the theoretically most advanced type of AI and would possess an understanding of the world, others, and itself. To complicate matters, researchers and philosophers also can’t quite agree whether we’re beginning to achieve AGI, if it’s still far off, or just totally impossible. For example, while a recent paper from Microsoft Research and OpenAI argues that Chat GPT-4 is an early form of AGI, many other researchers are skeptical of these claims and argue that they were just made for publicity [2, 3]. As the algorithms continue to advance, gen

ai will unlock new possibilities across industries. The additional costs that Gen Zers face have led them to amass more credit card debt than millennials at the same age, according to a TransUnion study last month. Gen Zers ages 22 to 24 had an average credit card balance of $2,834 late last year, compared with an inflation-adjusted $2,248 for millennials at the same age in late 2013.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators. While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

The push to produce a robotic intelligence that can fully leverage the wide breadth of movements opened up by bipedal humanoid design has been a key topic for researchers. The use of generative AI in robotics has been a white-hot subject recently, as well. New research out of MIT points to how the latter might profoundly affect the former. Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on.

It’s for Real: Generative AI Takes Hold in Insurance Distribution

You can foun additiona information about ai customer service and artificial intelligence and NLP. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations. Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement. If 2023 was the year the world discovered generative AI (gen AI), 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago.

Furthermore, by training Generative AI on historical documents and identifying patterns and trends, you can have it tailor pricing and coverage recommendations. The technology could also be used to create simulations of various scenarios and identify potential claims before they occur. This could allow companies to take proactive steps to deter and mitigate negative outcomes for insured people. This AI-enhanced assistant efficiently handles queries about insurance and pensions. Bot’s integration of Generative AI improves accuracy and accessibility in consumer interactions.

He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, https://chat.openai.com/ running and evaluating benchmarks. Generative AI models, like most deep learning models, are often referred to as “black boxes” because their decision-making processes are not easily understandable by humans.

A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Machines built in this way don’t possess any knowledge of previous events but instead only “react” to what is before them in a given moment. As a result, they can only perform certain advanced tasks within a very narrow scope, such as playing chess, and are incapable of performing tasks outside of their limited context. Weak AI, meanwhile, refers to the narrow use of widely available AI technology, like machine learning or deep learning, to perform very specific tasks, such as playing chess, recommending songs, or steering cars. Also known as Artificial Narrow Intelligence (ANI), weak AI is essentially the kind of AI we use daily. The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function.

In practice, this could be setting up systems where feedback loops are integral and inform continuous improvement and adaptation. All of these efforts can produce a coherent analytics and technology strategy that addresses all aspects of the business, with a keen eye on both value creation and differentiation. But leading insurers recognize the potential of GenAI as a catalyst for transformation. They look beyond individual use cases, focus on the big wins, and deploy GenAI to redesign their operating model end to end. By embracing this transformative approach, these leaders are rapidly pulling ahead of their competitors. He led technology strategy and procurement of a telco while reporting to the CEO.

They also know that innovation is a journey that requires ongoing effort, investment, and most importantly, a willingness to embrace change at all levels of the organization. While there are risks to every technology wave, the biggest risk could be missing the opportunity to shape what’s possible in insurance. The insurance workforce is already accustomed to using low or no code apps, so it’s not a massive leap to see them using AI to augment tasks through AI colleagues and co-pilots. For instance, AI-driven chatbots and virtual assistants are streamlining customer queries and claims processing, providing quick and CX-friendly responses 24/7. In addition to being able to understand and implement AI technologies, carriers also need to develop strategic responses to coming macrolevel changes. As many lines shift toward a “predict and prevent” methodology, carriers will need to rethink their customer engagement and branding, product design, and core earnings.

Regardless of how far we are from achieving AGI, you can assume that when someone uses the term artificial general intelligence, they’re referring to the kind of sentient computer programs and machines that are commonly found in popular science fiction. The increasing accessibility of generative AI tools has made it an in-demand skill for many tech roles. If you’re interested in learning to work with AI for your career, you might consider a free, beginner-friendly online program like Google’s Introduction to Generative AI. Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them. Also, responses suggest that companies are now using AI in more parts of the business.

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With AI Workbench, Lenovo’s AI-Ready mobile workstation enhances productivity and efficiency across various workflows – from data science to generative AI fine-tuning and inference. Claims organizations increase their focus on risk monitoring, prevention, and mitigation. IoT and new data sources are used to monitor risk and trigger interventions when factors exceed AI-defined thresholds. Customer interaction with insurance claims organizations focuses on avoiding potential loss. Individuals receive real-time alerts that may be linked with automatic interventions for inspection, maintenance, and repair.

Lenovo Unleashes AI-Powered Creativity and Productivity Devices and Solutions at CES 2024

In the long run, the improvements to risk management offered by Generative artificial intelligence solutions can save insurance businesses a lot of time and money. However, its impact is not limited to the USA alone; other countries, such as Canada and India, are also equipping their companies with AI technology. For instance, Niva Bupa, one of the largest stand-alone health insurance companies in India, has invested heavily in AI.

The new ThinkPad P1 Gen 7, P16v i Gen 2, P16s i Gen 3 and P14s i Gen 5 mobile workstations are equipped with cutting-edge components that enable top-notch performance. Together with Intel, Lenovo has significantly enhanced the performance and capability of the latest ThinkPad P series by including the innovative Intel Core Ultra processors up to Core Ultra 9 185H. These processors boast a combined integrated multi-processor package consisting of the CPU, NPU, and integrated GPU, designed to optimize the performance of AI features in over 100 applications4. The goal of this specific work is the creation of intelligence systems that allow robots to swap different tools to perform different tasks. The proliferation of multi-purpose systems would take the industry a step closer to general-purpose dream.

Gen Z’s median inflation-adjusted wages have been higher than those of previous generations at the same age. And they’ve risen faster, at least partly offsetting the higher costs, Colyar says, citing Federal Reserve data. Many work in industries − such as restaurants, hotels and retail − that have boosted pay sharply in response to pandemic-related labor shortages. Auto insurance has leaped almost 23% in the past year, and young gen ai in insurance Americans typically pay higher premiums because insurance companies believe they’re more likely to get into accidents and make poor decisions. Bloomberg Law sells legal research tools and software, including some that make use of generative AI. “It’s going to be incredibly important for new lawyers to understand what technologies are available and how to use them,” said Kathleen Orr, Orrick’s head of practice innovation.

AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

An insurer should start with use cases where risk can be managed within existing regulations, and that include human oversight. Invest in incentives, change management, and other ways to spur adoption among the distribution teams. Claims processing has traditionally been a time-consuming and labor-intensive task. GenAI can automate several stages of the claims journey, significantly reducing the time and effort required. (See Exhibit 2.) Typical use cases would include the preparation of standard mailings to claimants and the preparation of engagement letters to external service providers. This can enrich the role of adjusters by freeing them to focus on higher-value activities.

Lenovo’s ThinkPad P1 Gen 7, P16v i Gen 2, P16s i Gen 3, and P14s i Gen 5, with their cutting-edge AI technologies, are set to transform the way professionals engage with AI workflows. By collaborating with industry partners, Intel®, NVIDIA®, and Micron®, Lenovo has introduced powerful and performance-packed AI PCs that meet the demands of modern-day AI-intensive tasks. The inclusion of the Intel® Core™ Ultra processors with their integrated neural processing unit (NPU) and NVIDIA RTX™ Ada Generation GPUs signifies a major advancement in AI technology, boosting overall performance and productivity capabilities. By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start.

The global insurance industry has had a tough time since the onset of the pandemic, but averages do not tell the full story. As a simple example of how these building blocks complement one another, you can think of the knowledge assistant as a combination of the search and summary functions. First, it retrieves relevant information from various sources, before synthesizing the information into simple terms ready to share with customers. The knowledge assistant provides a user-friendly experience, delivering valuable insights and clarifying complex information.

We seem to finally be waking up to the dangers, and we’re getting an assist from an emerging technology. The use of generative AI for coding for in-house applications is set to be the next big thing in 2024. AI leveraged in this way can drastically cut the time it takes to assemble a group benefits quote, contributes to improved close ratios and fosters better relationships with your distribution partners.

• These tools can be trained to learn an insurance company’s products, policies and general “language,” helping customers fully understand their benefits plan.
• 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.
• Bill Wong, AI Research FellowBill Wong is an AI Research Fellow at Info-Tech Research Group, where he leads research focused on generative AI, AI strategy, responsible AI, and AI regulations and legislation.
• Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030.
• GenAI can also dive into mundane details faster and more efficiently than people.

Current insurance coverage descriptions and FAQs often leave clients seeking more clarity. When an insured encounters unique request scenarios, digital assistants can analyze complex policy details and address emotional nuances. These instruments deliver customized explanations and pinpoint pertinent sections.

To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent Chat GPT of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Generative AI is rapidly transforming the US insurance industry by offering a multitude of applications that enhance efficiency, operations, and customer experience. Generative AI in life insurance opens new avenues for enhancing customer support, as demonstrated by MetLife’s innovative application. Generative AI automates routine insurance tasks, enhancing efficiency and accuracy.

In essence, the demand for customer service automation through Generative AI is increasing, as it offers substantial improvements in responsiveness and customer experience. Another way Generative AI could help with risk assessment is by aiding coders in creating statistical models. This ability can speed up the programming work, requiring companies to hire fewer software programmers overall.