Insurance 2030 The impact of AI on the future of insurance

Operating model capability maturation requirements will need to be addressed, and CEOs will have a key role in helping lead their executive teams on this effort. To unlock the full value of AI, matured capabilities and well-coordinated risk and governance models will be imperative. The insights and services we provide help to create long-term value for clients, people and society, and to build trust in the capital markets. Enabled by data and technology, our services and solutions provide trust through assurance and help clients transform, grow and operate. Specifically, generative AI is ushering in new opportunities for insurers across the value chain — from strategy and product design, marketing and distribution, pricing and underwriting to claims and operations, and  governance. IoT sensors and an array of data-capture technologies, such as drones, largely replace traditional, manual methods of first notice of loss.

Gen AI has the potential to reshape the insurance value chain, enhancing productivity and delivering increased customer satisfaction. From product design and development to underwriting processes and claims management, the possibilities are endless. 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. The announcements include new AI PC innovations across Lenovo’s Yoga™, ThinkBook™, ThinkPad™, ThinkCentre™, and Legion™ sub-brands that personalize the computing experience for both consumers and businesses like never before. Two new proof of concept products, a tablet, software app, Motorola AI features, accessories, and more, round out the robust new portfolio of technology solutions. Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool.

Implement a governance structure for AI and gen AI that ensures sufficient oversight, authority, and accountability both within the organization and with third parties and regulators. This approach should include a definition of all roles and responsibilities in AI and gen AI management and the development of an incident management plan to address any issues that may arise from AI and gen AI use. The governance structure should be robust enough to withstand changes in personnel and time but also agile enough to adapt to evolving technology, business priorities, and regulatory requirements. Organizations may face large legal, reputational, organizational, and financial risks if they do not act swiftly.

Generative AI systems are developed based on prompts and extensive pre-training on large datasets. Essentially, Generative AI generates responses to prompts by identifying patterns in existing data across various domains, using domain-specific LLMs. This approach enhances insured satisfaction and positions businesses for market leadership. The benefits also include faster claims resolution, fewer errors, and a more engaged client base. It heralds an era where the insurer transitions from a mere transactional entity to a trusted advisor.

The first is to focus on a few game-changing applications and scale them across the value chain. For large parts of the company, these GenAI applications have a substantial impact on day-to-day operations. Knowledge assistants dramatically cut the time required to research documented knowledge. Using a chatbot interface, they provide agents with information from policy documents, wiki sites, and process manuals.

GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. Reinsurance, a crucial part of the insurance ecosystem, involves insurers transferring a portion of their risk to other insurance companies.

Navigating the Pitfalls of Generative AI in Insurance

The big win often involves combining multiple AI technologies to address different aspects of a project, such as semantic searching or language capabilities. Generative AI is a type of artificial intelligent system capable of generating new content. It does more than retrieve pre-determined answers (which makes it generative) and is enabled by models that identify, map, and derive context from patterns within the data inputs. The science behind the technology analyzes content from large sets of information (data sets, internet, etc.) and learns and improves performance even with unlabeled and unstructured data. Generative AI can map patterns and connections within the data inputs, allowing it to understand the essence and context of an object. The technology uses advanced natural language and responds in a more conversational speaking style.

They can present answers in layman’s terms, ready to be shared directly with customers. This application is highly scalable, proving useful across claims and customer service teams as well as across lines of business. Similarly, a coding assistant accelerates software development by offering autocompletion, code translation, and debugging capabilities. It has the potential to remove bottlenecks in IT capacities throughout the value chain and help address the difficulties of dealing with legacy code and mainframe programming languages.

Notwithstanding those risks, many law firms, traditionally slow movers when it comes to new technology, have embraced generative AI. Firms such as A&O Shearman and Paul Weiss are customers of the generative AI legal startup Harvey, which has received more than $100 million in funding to date. Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI.

What generation is most affected by inflation?

The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act upon such information without appropriate professional advice after a thorough examination of the particular situation. At the core of any successful IT modernization, you’ll find a solid data foundation. An exclusive KPMG survey shows how financial services leaders are approaching this transformative technology.

At the end of the day, it’s impossible to list all of the potential use cases for Generative Artificial Intelligence & ChatGPT in the insurance industry since the technology is always evolving. That said, these are some of the most obvious ways to implement Generative AI power in the insurance business, and insurance companies that don’t start trying them will be left behind by companies that do. Such hyper-personalization goes beyond convenience, building trust and loyalty among customers. Insurers, by showing a deep understanding of individual needs, strengthen their relationships with the audience. Additionally, artificial intelligence’s role extends to learning platforms, where it identifies specific knowledge gaps among agents. It then delivers targeted training, enhancing employee expertise and ensuring compliance.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Ella’s nudges resulted in nearly two million additional interactions from plan members early in the pandemic from January to September 2020. During high-load periods, the volume of quotes requiring underwriter review can slow processes due to an inefficient allocation of human resources. This acts as a powerful benchmarking tool while circumventing the data limitations in group insurance because it uses the carrier’s own data.

Major gen AI companies, for example, have lost significant market value when their platforms were found hallucinating (when AI generates false or illogical information). Therefore, insurance companies must invest in educational campaigns to inform their clients about the benefits and security measures of Generative AI. Equally important is the need to ensure that these AI systems are transparent and user-friendly, fostering a comfortable transition while maintaining security and compliance for all clients. According to the FBI, $40 billion is lost to insurance fraud each year, costing the average family $400 to $700 annually. Although it’s impossible to prevent all insurance fraud, insurance companies typically offset its cost by incorporating it into insurance premiums. For example, Generative AI in banking can be trained on customer applications and risk profiles and then use that information to generate personalized insurance policies.

The number of agents is reduced substantially as active agents retire and remaining agents rely heavily on technology to increase productivity. The agent of the future can sell nearly all types of coverage and adds value by helping clients manage their portfolios of coverage across experiences, health, life, mobility, personal property, and residential. Agents use smart personal assistants to optimize their tasks as well as AI-enabled bots to find potential deals for clients.

Asset management was slower to embrace the transformational

power of technology. Market insights and forward-looking perspectives for financial services leaders and professionals. KPMG firms are excited about AI’s opportunities and equally committed to deploying the technology in a way that is responsible, trustworthy, safe, and free from bias. KPMG Trusted AI, is our strategic approach and framework to designing, building, deploying, and using AI solution in a responsible and ethical manner so we can accelerate value with confidence. Ilanit Adesman-Navon, Head of Insurance and Fintech at KPMG in Israel, highlights how AI can be used to guide ‘next best offer’ in more sophisticated ways. AI can be trained to understand sentiment, empathize with the customer situation, then guide agents to the most relevant, personalized offers — all of which could be done in real time”.

These models can predict if a new claim has a high chance of being fraudulent, thereby saving the company money. Furthermore, the ThinkPad P1 Gen 7 is the world’s first mobile workstation2 to include LPDDR5x LPCAMM2 memory, up to 64GB. LPCAMM2, which is brought to market by Micron in collaboration with Lenovo, delivers one of the fastest energy efficient modular memory solutions for PCs. Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions.

In just 6 hours, you’ll gain foundational knowledge about AI terminology, strategy, and the workflow of machine learning projects. He says the cost of services hit hardest by inflation should rise more slowly or stabilize in the coming months. Rent for new leases has dropped, but that change has been slow to filter through to tenants on existing leases.

Taking the streaming and collaboration experience to a new level, select Lenovo Legion systems,7 including the Legion 7i (16”, 9) and Legion 5i (16”, 9) are now equipped with AvatarMaster. A new app powered by AI, AvatarMaster transforms users’ profiles into a 3D digital avatar, with gen ai in insurance complete customization capabilities from appearance and facial features to clothing and accessories. After creating and customizing their avatars, users can animate and stream a digital version of themselves during video conferences, gaming sessions, and across multiple platforms.

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. Generative AI has the potential to revolutionize the insurance industry, and those who can operationalize it responsibly will be at the forefront of this exciting journey towards the future of insurance. In 2023, generative AI took the spotlight, emerging as the most talked-about technology of the year.

For an individual insurer, the technology could increase revenues by 15% to 20% and reduce costs by 5% to 15%. Generative AI has the power to transform the insurance sector by increasing operational effectiveness, opening up new innovation opportunities and deepening customer relationships. Discover how EY insights and services are helping to reframe the future of your industry. Our perspectives on taking a CustomerFirst approach—realigning corporate strategy with investments that are deeply tied to customers’ needs. Our thought leadership for insurance leaders to drive new business growth and reinvent insurance solutions for customers.

QBE is using a Gen AI powered tool within its U.S. cyber insurance division to streamline the quote generation process, reviewing submissions and summarising the key points, speeding up the submission to quote process by 65%. AI is also able to rapidly identify similar risks on the carrier’s books and compare them with the risk being assessed. This helps set the price for the new policy, ensures compliance with the insurer’s strategy and underwriting guidelines, and improves the consistency of its book of business. Organizations could face fines from legal enforcement—of up to 7 percent of annual global revenues, according to the AI regulation proposed by the European Union, for example.

Although the term is commonly used to describe a range of different technologies in use today, many disagree on whether these actually constitute artificial intelligence. Instead, some argue that much of the technology used in the real world today actually constitutes highly advanced machine learning that is simply a first step towards true artificial intelligence, or “general artificial intelligence” (GAI). We’ve seen impressive headway in the industry already, with global insurers such as QBE turning to Gen AI to transform its underwriting function for better risk selection and to support rapid growth.

For example, insurers are unlikely to gain much insights from limited-scale IoT pilot projects in discrete parts of the business. Instead, they must proceed with purpose and an understanding of how their organization might participate in the IoT ecosystem at scale. Pilots and proof-of-concept (POC) projects should be designed to test not just how a technology works but also how successful the carrier might be operating in a particular role within a data- or IoT-based ecosystem. AI and its related technologies will have a seismic impact on all aspects of the insurance industry, from distribution to underwriting and pricing to claims. Advanced technologies and data are already affecting distribution and underwriting, with policies being priced, purchased, and bound in near real time. An in-depth examination at what insurance may look like in 2030 highlights dramatic changes across the insurance value chain.

To make the transformation possible, CEOs have a unique opportunity to meet this innovation mandate by fusing GenAI into their overall business strategy and activating required cultural shifts. Recent developments in AI present the financial services industry with many opportunities for disruption. Insurers must take an intentional approach to adopting generative AI, introducing it to the organization with a focus on use cases. Because generative AI carries potential risks, such as bias, human oversight plays a key role in its responsible deployment. Because its algorithms are designed to enable learning from data input, generative AI can produce original content, such as images, text and even music, that is sometimes indistinguishable from content created by people. Some or all of the services described herein may not be permissible for KPMG audit clients and their affiliates or related entities.

As governments and regulators try to define what such a control environment should look like, the developing approaches are fragmented and often misaligned, making it difficult for organizations to navigate and causing substantial uncertainty. Feel free to request a custom AI demo of one of our products today to learn more about them. We look forward to getting to know your business and matching it with the right Generative AI solution to help it grow. The holy grail for businesses, especially in the insurance sector, is the ability to drive top-line growth.

With AI, high resolution with high refresh rate 2D content instantly transforms into vivid 3D content with precise spatial reconstruction, regardless of how complex the backgrounds can be, and all without requiring additional power or system upgrades. The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. Integrating Conversational AI in insurance industry brings numerous benefits, including the potential for cost savings by reducing the need for live customer support agents.

Insurance transitions from a “purchase and annual renewal” model to a continuous cycle, as product offerings constantly adapt to an individual’s behavioral patterns. New products emerge to cover the shifting nature of living arrangements and travel. Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. His digital personal assistant orders him a a vehicle with self-driving capabilities for a meeting across town. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into “active” mode. Scott’s assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road.

Perhaps more important, adopting safeguards will help position the organization as a trusted provider. GenAI can transform the vast amount of data generated during the claims process into valuable insights. Insurers can identify trends, optimize processes, and make better, data-driven decisions when they bring unstructured data into an actionable form that integrates with their core platforms and subsystems. And by analyzing customer data with GenAI, insurers can identify patterns and preferences, allowing them to provide customers with tailored communications and a convenient, hyperpersonalized experience throughout the claims process. In this webcast, EY US and Microsoft leaders discuss how generative AI can fundamentally reshape the insurance industry, from underwriting and risk assessment, to claims processing and customer service.

The labour force reduction has led to increased costs for staff retention and rising payroll expenses. The Key Points at the top of this article were created with the assistance of Artificial Intelligence (AI) and reviewed by a journalist before publication. For both experienced and young lawyers, the biggest warning sign that comes with the new territory is the documented accuracy problems, or hallucinations, of AI models. An infamous example is two New York lawyers getting fined $5,000 last year after submitting a brief citing six nonexistent cases that one of the lawyers admitted were supplied by OpenAI’s ChatGPT. Alden noted the program highlights how legal training is “fundamentally” changing.

The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected. https://chat.openai.com/ When researching artificial intelligence, you might have come across the terms “strong” and “weak” AI. Though these terms might seem confusing, you likely already have a sense of what they mean.

Within Organization Strategy

Post-pandemic behaviour has seen more reckless driving patterns as people readjust to regular road traffic​​. Higher accident frequencies result in more claims, prompting insurers to raise premiums to cover these costs. K&L Gates and Orrick each partner with tech learning platform AltaClaro for firmwide trainings on subjects such as “prompt engineering,” regarded as the science of interacting with generative AI models and how the technology works.

GenAI—coupled with machine learning, natural language processing, and other cutting-edge technologies—promises to enhance accuracy, efficiency, and customer satisfaction. While it is too early to assess the full potential of GenAI to reduce costs in the claims arena, we are observing substantial interest among nearly all large insurers to experiment. Insurance industry and it will likely force innovation in many areas.” Yet, a reliance on legacy systems poses a challenge to innovation. While existing technologies provided the level of support previously required, and gave stability during the global pandemic to help insurers weather macroeconomic pressures, the same systems could now be holding them back.

It could also mean making transparency the norm or simply asking people what they need and encouraging everyone to contribute ideas. At the very least, it’s investing in training and development that help employees understand how to apply these new technologies effectively to benefit both personal and organizational productivity. Insurance companies are already transforming their operations, exploring new technologies and in some cases leading the charge on AI. From back office to front office, insurance functions can see potential benefits in automating claims handling, enhancing fraud detection, and optimizing agent and contact center operations.

• Consequently, it frees staff to focus on more strategic, customer-centric duties.
• Notwithstanding those risks, many law firms, traditionally slow movers when it comes to new technology, have embraced generative AI.
• Her insights have appeared in various industry outlets, including CIO, InformationWeek, and Technology Magazine.
• Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.
• 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.
• Info-Tech LIVE 2024 promises actionable insights and transformative strategies for IT leaders and professionals.

In the US, the average cost of full coverage car insurance rose to $2,543 per year, a 26% increase over the past year. Similarly, in the UK, the Association of British Insurers (ABI) reported a 25% increase in motor insurance premiums in 2023 compared to the previous year. The cost of repairing vehicles has surged due to higher prices for parts and labour. Modern cars, equipped with advanced technologies such as sensors and cameras, are also more expensive to fix.

Price remains central in consumer decision making, but carriers innovate to diminish competition purely on price. Sophisticated proprietary platforms connect customers and insurers and offer customers differentiated experiences, features, and value. In some segments, price competition intensifies, and razor-thin margins are the norm, while in other segments, unique insurance offerings enable margin expansion and Chat GPT differentiation. In jurisdictions where change is embraced, the pace of pricing innovation is rapid. Pricing is available in real time based on usage and a dynamic, data-rich assessment of risk, empowering consumers to make decisions about how their actions influence coverage, insurability, and pricing. Smart contracts enabled by blockchain instantaneously authorize payments from a customer’s financial account.

In this episode, Ed Chanda, KPMG National Sector Leader for Insurance, and Kelly Combs, KPMG Managing Director, Trusted AI, explore the exciting potential of generative AI in the insurance industry. By packaging existing techniques in a different way, generative AI brings data science closer to business stakeholders, allowing them to more intuitively interact with and receive insights from AI. Artificial intelligence (AI) isn’t new in insurance — existing use cases are seen across risk modeling, data forecasting, claims handling and contact center operations, with an abundance of potential opportunities in the pipeline. Highly dynamic, usage-based insurance (UBI) products proliferate and are tailored to the behavior of individual consumers.

QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe. The future of insurance includes AI, which is poised to revolutionize the group insurance industry, providing significant benefits to both insurers and customers. Thanks to Ella’s interventions at the right time to the right user, the company experienced an 83% increase in additional coverage purchased compared with the previous year.

By 2030, a much larger proportion of standard vehicles will have autonomous features, such as self-driving capabilities. Carriers will need to understand how the increasing presence of robotics in everyday life and across industries will shift risk pools, change customer expectations, and enable new products and channels. In addition, GenAI offers new ways of improving the experience for customers, distribution partners, and employees. Tools such as GenAI-powered sentiment analyzers have the potential to greatly enhance customer service by empowering agents to consistently apply best practices of empathetic customer care.

That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced. For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will. While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year.

The learning curve is steep, but thoughtful, fast-moving retailers will set new standards for consumer experiences and create an advantage. Enable life insurance agents to better prioritize and customize outreach as well as meet client needs. KPMG has market-leading alliances with many of the world’s leading software and services vendors. Contact your local member firm to talk through insights from this article, or to discuss your unique technology and AI requirements. When Scott pulls into his destination’s parking lot, his car bumps into one of several parking signs.

Our Reinventing Insurance podcast explores best practices for taking a CustomerFirst approach to innovation within Insurance. Use the RFP submission form to detail the services KPMG can help assist you with. By submitting, you agree that KPMG LLP may process any personal information you provide pursuant to KPMG LLP’s Privacy Statement. The KPMG 2023 Insurance CEO Outlook also highlights a significant degree of trust in AI with 58 percent of CEOs in insurance feeling confident about achieving returns on investment within five years.

For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage. In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management. For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6).

Based on Bain’s 10 years of research, five themes describe the progress and challenges of earning customers’ advocacy in an increasingly digital experience. Over the medium and long term, insurers should not use generative AI as a substitute for organizational IQ. The right GenAI architecture should instead ensure the constant development of employees’ professional skills. GenAI should accelerate claim handlers’ acquisition and absorption of experience, not impede it or replace it.