Generative AI (GAI) – you might have heard of it – is one of the most exciting and promising digital technologies in recent years. As it’s capable of transforming the way insurance companies execute core business functions, engage customers and deliver innovative technological solutions, we cannot overstate the extent to which it will impact the sector.
In this piece, we discuss how GAI will change the insurance sector, explore the wider context in which insurance AI is developing, and examine the implementation process.
Embracing the Possibilities of AI
Over the coming years, AI will have a pronounced effect on society and commerce, changing how we live and work and shaking up industries of all types and sizes.
The emergence and rapid popularization of Large Language Models (LLMs) and Generative AI applications indicate the speed at which GAI is developing. Fresh innovations come thick and fast, with new ideas, opportunities and capabilities circulating at a remarkable rate.
Momentum is building, and businesses are bracing for significant disruption. Not since the emergence of the internet has a technology demonstrated so much potential for change.
As the insurance sector relies heavily on natural language, this new generation of AI technologies is particularly potent. We believe insurance companies can leverage these emerging technologies to realize significant improvements across the value chain.
But to do so, insurance organizations must prepare and ensure a carefully considered and coherent AI strategy is in place.
Generative AI: The Time is Now
One of the defining features of GAI technology is the speed at which it is developing and evolving.
AI is not a new technology. We can trace its origins back to the 1950s. Nor is its use in the insurance industry new: Rules-based AI and predictive modeling had a considerable impact over the last decade.
GAI represents a fundamental shift in the insurance sector’s relationship with AI. It will affect almost every aspect of insurance operations, from price-setting to the development of technological solutions. As the insurance sector is dependent on a wide range of knowledge workers, such as underwriters, claims adjusters, finance managers and software designers, companies can utilize GAI to augment and complement human expertise across their organizations.
There is a palpable sense of urgency. GAI will transform the insurance sector, and it will do so quickly. Insurance organizations must consider how they can leverage these new technologies to avoid falling behind and begin building toward this new future.
Generative AI and the Insurance Sector
To better understand how the insurance sector will leverage GAI, we identified four core use areas.
1. Customer engagement and servicing
2. Insight-driven underwriting and claims
3. Financial performance optimization
4. Technology solution engineering and delivery
Customer engagement and servicing typically refers to the front-end, customer-facing applications. In this context, GAI will help refine and improve agent performance while enabling insurance companies to deliver a more personalized and intuitive customer experience.
Insight-driven underwriting and claims involves using GAI to advise on risk mitigation and some aspects of underwriting. It may help to identify risks that have not yet been considered and enable faster decision-making and more granular underwriting rules. GAI will also likely play a role in fraud detection.
GAI will also support financial performance optimization. In this context, it will facilitate improved comparative analytics, benchmarking, and impact modeling to maximize the financial benefits associated with certain decisions. Enhanced data analysis will also allow companies to spot emerging trends sooner and pivot their approach more quickly in response to shifting market conditions.
When it comes to technology solution engineering and delivery, GAI is already having a considerable impact. GAI applications can assist with defining, writing and refactoring code and new “co-pilot” models will make talented developers even more capable, productive and efficient.
Implementation within an Insurance Context
When considering GAI technology, insurance companies need a comprehensive understanding of the issues surrounding AI implementation.
Currently, insurance organizations have to engage with three core implementation issues.
1. Transparency, regulations and compliance
2. Responsible and trustworthy AI
3. Organization-wide innovation
The insurance sector is heavily regulated and businesses are subject to significant financial penalties should they fail to comply with regulations. This means transparency is a core concern. However, insurance companies should not view compliance as an obstacle.
Instead, it is an opportunity. Forward-thinking, industry-leading insurance companies have the chance to shape the discourse surrounding AI in insurance and help embed responsible and ethical AI practices in the transformation process.
Insurance companies must recognize that effective AI implementation depends on enacting organization-wide changes. It is not solely an engineering concern and successful transformation will require a shift in culture and cross-functional governance.
A Pivotal Moment in the Insurance Sector
Implementing new technologies is complex. Integrating GAI technologies even more so.
Rapid development means insurance companies need to move quickly and begin identifying pilot programs and PoCs (proof of concepts) that demonstrate the technology’s value.
At the same time, businesses must lay the foundations for an organization-wide shift toward GAI.
Identifying the right use cases and crafting a long-term strategy often requires input from AI specialists and technology experts. To assist insurance companies committed to innovation, EPAM wrote Engineering the Future of Insurance: How Generative AI is Disrupting the Insurance Market & Impacting Ways of Working.
The piece expands on many of the key themes in this blog and explores GAI implementation in much greater depth. In doing so, it touches on the way insurance companies can identify appropriate use cases to leverage GAI in the context of both quick wins and long-term transformation.
