Global Generative AI in Insurance Market size will be worth $5,5 bn by 2032 from its current size of $346.3 mn, and growing at a CAGR of 32.9% through the next decade.

The insurance market is undergoing a remarkable transformation, thanks to the exponential growth of generative artificial intelligence (see How AI Technology Can Help Insurers).

Insurance providers are harnessing the power of artificial intelligence to optimise their operations, improve risk assessment models, and deliver personalised customer experiences.

The revolutionary capabilities of generative AI, which generates new and valuable information, are poised to reshape this industry sector.

Generative AI in Insurance

Generative AI in Insurance

As the insurance industry continues to navigate the pace of change, complexity and uncertainty in our world, consumers continue to respond, expecting companies to be more responsive to their needs. This year’s underwriting predictions offer guidance on how carriers can respond faster.

With Data-Driven AI models, insurance companies can do more personalized recommendations to consumer as well as to build the appropriate products for segments of clients by optimizing earnings and customer satisfaction

AI can also determine an individualized price based on consumer behavior and historical data (see how Using AI, Analytics & Cloud to Reimagine the Insurance Value Chain).

As insurers modernize their legacy core systems, freeing siloed data, they’re able to automate their underwriting workflows to provide a faster digital buying experience, while connecting to additional data sources that help them apply the appropriate level of risk management.

The expansion of the generative AI market in the insurance industry can be largely attributed to its significant impact on operational efficiency. Insurers are increasingly adopting AI algorithms to streamline critical processes such as claims processing, underwriting, and policy administration.

By automating these tasks through artificial intelligence, generative AI plays a crucial role in enhancing operational efficiency.

Furthermore, the ability of generative AI to generate fresh data empowers insurers to make faster and more informed decisions, reducing the need for manual interventions and ultimately improving overall operational efficiency.

How Generative AI technology is reshaping insurance?

What are the Benefits of Generative AI?

Generative AI technology is reshaping insurance by enhancing risk analysis, pricing, and customer experiences. It leverages historical data to improve pricing accuracy and optimise risk management strategies.

Let’s take a closer look at the impact of AI on the future of insurance. The risks insurers cover and the ways they underwrite, distribute, and manage claims are also changing.

In an increasingly digitalized world some risks will become less frequent, while others, like cyber, will gain in importance, and again others may cease to exist.

  • Artificial intelligence (AI) can help insurers assess risk, detect fraud and reduce human error in the application process. The result is insurers who are better equipped to sell customers the plans most suited for them.
  • Customers benefit from the streamlined service and claims processing that AI affords.
  • Some insurers think that, as machine learning progresses, the need for human underwriters could become a thing of the past – but that day might be years away.

There are several ways in which insurance companies can tackle these challenges. One of the best ways forward would be to invest in technology solutions powered by artificial intelligence in insurance.

What is generative AI?

What is generative AI?

Generative AI enables users to quickly generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data.

Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content

One of the breakthroughs with generative AI models is the ability to leverage different learning approaches, including unsupervised or semi-supervised learning for training.

By detecting patterns and improving fraud detection, generative AI provides precise risk assessments through simulation models. It also utilises customer data to deliver personalised recommendations and tailored offerings, enhancing satisfaction and retention. This transformative technology drives performance and customer-centricity in the insurance industry.

Global generative AI market size 2022-2032

The global generative AI market size was valued at $10.14 bn in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 35.6% from 2023 to 2032 to $118 bn.

Global generative AI market size
Global generative AI market size

Factors, such as the rising applications of technologies, such as super-resolution, text-to-image conversion, & text-to-video conversion, and growing demand to modernize workflow across industries are driving the demand for generative AI applications among industries.

Microsoft launched a model, Visual ChatGPT, which comprises multiple visual foundation models and enables users to interact with ChatGPT through graphical user interfaces.

Generative AI makes use of unsupervised learning algorithms for spam detection, image compression, and preprocessing data stage, such as removing noise from visual data, to improve picture quality.

Moreover, supervised learning algorithms are used for medical imaging and image classification. Furthermore, it has applications in various industries, such as BFSI, healthcare, automotive & transportation, IT & telecommunications, media & entertainment, and others.

Generative AI is a powerful tool that can be used to create new ideas, solve problems, and create new products. Moreover, it can help organizations save money and time, increase efficiency, and enhance the quality of content generated.

Generative AI’s impact on labor productivity

Breakthroughs in generative artificial intelligence have the potential to bring about sweeping changes to the global economy, according to Goldman Sachs Research.

As tools using advances in natural language processing work their way into businesses and society, they could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period.

Despite significant uncertainty around the potential for generative AI, its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects

Joseph Briggs, Goldman Sachs economists

A new wave of AI systems may also have a major impact on employment markets around the world. Shifts in workflows triggered by these advances could expose the equivalent of 300 million full-time jobs to automation (see 9 New Technology Trends by Insurance Sector).

Effect of AI adoption on annual labor productivity growth, 10-year adoption period
Source: Goldman Sachs Research

Software companies are already arming their product portfolios with new generative AI offerings. Software-as-a-service firms, for example, are using it to open opportunities for upselling and cross-selling product and increasing their customer retention and expansion, the authors note.

They see multiple ways that such businesses can leverage generative AI for growth:

1) through new production and application releases,

2) by charging premiums for AI-integrated offerings

3) by increasing prices over time as existing products are supplemented with AI-enabled features and prove their value to customers.

Added up, GS Research estimates the total addressable market for generative AI software to be $150 billion, compared with $685 billion for the global software industry.

As more generative AI tools are developed and layered into existing software packages and technology platforms, the team sees businesses across the economy benefiting, from enhancing office productivity and sales efforts, to the design of buildings and manufactured parts, to improving patient diagnosis in healthcare settings, to detecting cyber fraud.

How to Evaluate Generative AI Models?

The three requirements of a successful generative AI model

How to Evaluate Generative AI Models?

What is an example of generative AI?

Generative AI is used in any algorithm/model that utilizes AI to output a brand new attribute. Right now, the most prominent examples are ChatGPT and DALL-E.

How Does Generative AI Work?

Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content.

One of the breakthroughs with generative AI models is the ability to leverage different learning approaches, including unsupervised or semi-supervised learning for training. This has given organizations the ability to more easily and quickly leverage a large amount of unlabeled data to create foundation models. As the name suggests, foundation models can be used as a base for AI systems that can perform multiple tasks. 

Examples of foundation models include GPT-3 and Stable Diffusion, which allow users to leverage the power of language. For example, popular applications like ChatGPT, which draws from GPT-3, allow users to generate an essay based on a short text request. On the other hand, Stable Diffusion allows users to generate photorealistic images given a text input.

What are the Benefits of Generative AI?

How Generative AI technology is reshaping insurance?

Generative AI is important for a number of reasons. Some of the key benefits of generative AI include:

  1. Generative AI algorithms can be used to create new, original content, such as images, videos, and text, that’s indistinguishable from content created by humans. This can be useful for applications such as entertainment, advertising, and creative arts.
  2. Generative AI algorithms can be used to improve the efficiency and accuracy of existing AI systems, such as natural language processing and computer vision. For example, generative AI algorithms can be used to create synthetic data that can be used to train and evaluate other AI algorithms.
  3. Generative AI algorithms can be used to explore and analyze complex data in new ways, allowing businesses and researchers to uncover hidden patterns and trends that may not be apparent from the raw data alone.
  4. Generative AI algorithms can help automate and accelerate a variety of tasks and processes, saving time and resources for businesses and organizations.

Overall, generative AI has the potential to significantly impact a wide range of industries and applications and is an important area of AI research and development.

What are the Challenges of Generative AI?

As an evolving space, generative models are still considered to be in their early stages, giving them space for growth in the following areas.

  1. Scale of compute infrastructure: Generative AI models can boast billions of parameters and require fast and efficient data pipelines to train. Significant capital investment, technical expertise, and large-scale compute infrastructure are necessary to maintain and develop generative models. For example, diffusion models could require millions or billions of images to train. Moreover, to train such large datasets, massive compute power is needed, and AI practitioners must be able to procure and leverage hundreds of GPUs to train their models.
  2. Sampling speed: Due to the scale of generative models, there may be latency present in the time it takes to generate an instance. Particularly for interactive use cases such as chatbots, AI voice assistants, or customer service applications, conversations must happen immediately and accurately. As diffusion models become increasingly popular due to the high-quality samples that they can create, their slow sampling speeds have become increasingly apparent.
  3. Lack of high-quality data: Oftentimes, generative AI models are used to produce synthetic data for different use cases. However, while troves of data are being generated globally every day, not all data can be used to train AI models. Generative models require high-quality, unbiased data to operate. Moreover, some domains don’t have enough data to train a model. As an example, few 3D assets exist and they’re expensive to develop. Such areas will require significant resources to evolve and mature.
  4. Data licenses: Further compounding the issue of a lack of high-quality data, many organizations struggle to get a commercial license to use existing datasets or to build bespoke datasets to train generative models. This is an extremely important process and key to avoiding intellectual property infringement issues.

Many companies have a goal to support the continued growth and development of generative AI models with services and tools to help solve these issues. These products and platforms abstract away the complexities of setting up the models and running them at scale.


Edited & Fact checked by Oleg Parashchak   Oleg Parashchak

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