Viewpoint: Balancing the opportunities and risks of generative AI

Artificial intelligence (AI), in its present form, has proven invaluable in insurance, providing more accurate data insights, enhancing operational efficiency and fostering innovation.

Industry applications today predominantly rely on traditional AI methods with a focus on automating routine tasks and extracting insights from vast datasets. This technology has played a vital role in portfolio management, risk assessment, streamlining claims and submissions processing, making it more efficient for insurers and customers alike.

Increasing global demand for insurance services necessitates a continuous quest to optimise processes across the entire value chain. The emergence of more advanced generative AI tools proficient in handling unstructured data, such as ChatGPT, has the potential to help our industry innovate and develop cutting-edge solutions that drive operational and business efficiencies. We will go through a steep learning curve this year when it comes to applying generative AI – it is an exciting time to be at the confluence of insurance and digital technology.

Exploring generative AI use cases

Entering 2024, the opportunities and challenges of generative AI in insurance become more pronounced. The industry’s exploration of this technology is marked by a cautious optimism, recognising the need for a balanced approach. Concrete use cases are being tested, aligning with regulatory requirements and internal standards to ensure responsible and ethical deployment.

While traditional AI has already demonstrated its prowess in insurance, the industry is yet to explore generative AI’s full potential, while also keeping track of its emerging risks. At Swiss Re, we have been testing the capabilities of large language models (LLMs) for more than three years. Selected use cases have been deployed to pilot user groups and we expect to deploy them to a broader user base this year.

The emergence of more advanced generative AI tools proficient in handling unstructured data, such as ChatGPT, has the potential to help our industry innovate and develop cutting-edge solutions that drive operational and business efficiencies

On the operational side, generative AI is set to introduce significant digital workplace enhancements. We are collaborating with leading tech partners to equip our employees with AI assistants by embedding LLM capabilities into the workplace. Our aim is to continue driving employee efficiency and creativity and thus achieving better results for our clients. What is important is the users of this novel technology always remain in control; they decide when to use what kind of AI-powered outcomes in a secure environment.

From a business perspective, there are promising use cases applying LLMs to efficiently analyse and process large documents and datasets powered by advanced natural language processing (NLP) applications. Engineering high-quality data foundations is key to reaping the many future benefits LLMs may offer to drive efficiency across the insurance value chain. Also, it is paramount to ensure the proper guardrails are in place before releasing new AI-powered solutions, also to gain the trust of our clients and make them part of this journey.

Blending technology, talent and purpose

Our experience shows how important it is to have the necessary tech talent and expertise in-house to effectively develop, fine-tune and deploy such solutions at scale. As such, the rise of AI creates a huge demand for experts in the field in the race to harness the full potential of AI.

The significance of technology is para­mount – equally important is attracting top tech talents, which requires providing state-of-the-art tools and engaging them with challenging problems. This approach not only leverages the best of technology but also fosters a culture of continuous upskilling, essential for innovation.

Taking a strategic approach to tech talent acquisition and retention, we offer three key attractions for tech talents at Swiss Re: a strong link between tech and our business strategy; opportunities for individual development within our organisation; and a compelling purpose of making the world more resilient. Applying new technologies to address major global challenges, from climate change and natural catastrophes to healthcare crises and economic distress, makes working in our industry particularly interesting and rewarding.

As we embrace the potential of generative AI, it is crucial to acknowledge and address the potential risks as well. Clearly, any generative AI initiatives and projects must always be aligned with the ever-evolving risk landscape and regulatory requirements.

Overarching AI related risks with respect to data privacy, data protection and confidentiality remain. Additional risks, such as embedded bias and robustness of the results are either new or amplified by generative AI; so too are its capabilities to generate new content based on the training data.

A multifaceted approach to mitigating these risks helps establish a balance between leveraging this powerful technology while driving the development of ethical AI that aligns with our values and needs. Therefore, the focus on responsible use of generative AI and the prevention of biased outcomes – and wrong but plausible-sounding answers – through regular and stringent validation of AI models is paramount.

The generative AI journey holds the promise of unlocking new dimensions in risk insights, operational efficiency, and innovative solutions. However, it is our goal to steer this transformative technology towards a future where AI augments human knowledge for the greater good. The very promising opportunities AI opens to re/insurers rely on a harmonised interplay human expertise and intuition with creativity of generative AI.

As the insurance industry unveils the full potential and navigates the challenges of generative AI in 2024, it marks a significant chapter in the ongoing evolution of our sector.

Pravina Ladva is group chief digital and technology officer at Swiss Re