Florida insurance market continues to strengthen following impactful legislation  April 4th 2024

April ITL Focus: AI | Insurance Thought Leadership

We seem to be in a moment where we're taking a deep breath on generative AI and large language models like ChatGPT. The enthusiasm over the past year and a half has been extraordinary, and we can all see how AI will at least improve productivity and perhaps make more revolutionary changes in insurance. But we haven't seen any killer apps like the electronic spreadsheet that sold so many Apple II's in the late 1970s and 1980s and launched the personal computer revolution. And we're waiting.

We're not in what Gartner would call the Trough of Disillusionment on their Technology Hype Cycle. But we are in a Trough of Where Do We Go From Here?

When in doubt on breakthrough ideas, I often turn to my friend John Sviokla, whom I've had the pleasure of working with for more than a quarter-century. After years of teaching at Harvard Business School, John in the late 1990s became vice chairman of Diamond Management and Technology Consultants, where I was a partner. He stayed on after PwC bought Diamond in 2010 and was a leader in the strategy and innovation practice, before becoming the marketing lead in the U.S. A year ago, he and some colleagues formed GAI Insights to help companies think about the implications of generative AI and about how to begin implementing it, with insurance as a major focus. They have leaned in to their connections with Harvard, publishing two recent articles in Harvard Business Review; John and one of his colleagues have also become executive fellows at the business school.

At Diamond, we joked that we needed to assign a young associate to just follow John around with a grocery sack and collect all the ideas he spun off every day and didn't have the time to follow through on. So, while John and his colleagues are delving into many ideas and developing them deeply, I thought I'd get out a shopping bag and ask John to toss out some ideas about the future of generative AI. 

COMPLETE ARTICLE HERE

Paul Carroll, editor- in -chief, Insurance Thought Leadership

Navigating risks in AI governance – what have we learned so far?

Efforts are being made in a current regulatory void, but just how effective are they?

As artificial intelligence (AI) continues to evolve and become increasingly integrated into various aspects of business and governance, the importance of robust AI governance for effective risk management has never been more pronounced. With AI's rapid advancement come new and complex risks, from ethical dilemmas and privacy concerns to potential financial losses and reputational damage.

AI governance serves as a critical framework, ensuring that AI technologies are developed, deployed, and utilised in a manner that not only fosters innovation but also mitigates these emerging risks, thereby safeguarding organisations and society at large from potential adverse outcomes.

Sonal Madhok, an analyst within the CRB Graduate Development Program at WTW, delineates this transformative era where the swift integration of AI in various sectors has catalysed a shift from mere planning to action in the realm of governance. This surge in AI applications highlights a profound need for a governance framework characterised by transparency, fairness, and safety, albeit in the absence of a universally adopted guideline.

Kenneth Araullo is a senior news writer for Insurance Business

What is synthetic data, and how are insurers using it?

Advances in generative AI have enhanced the accuracy of synthetic data, which makes it a valuable tool.

By 2027, as many as 40% of the AI algorithms used by insurers will integrate synthetic data in order to ensure fairness within their processes and comply with regulations, a recent report from IDC FutureScape predicted.

Synthetic data is created artificially through computer simulation or generated by algorithms as either an alternative or supplement to real-world data. This data allows AI models to mirror real-world scenarios while also protecting the privacy of customers.

According to TechTarget, to create synthetic data generative AI models “use a set of training data to learn the statistical patterns and relationships in the data and then use this knowledge to generate new synthetic data that’s similar to the original data.”

Currently, about 18% of enterprises integrate synthetic data, notably to help insurers comply with privacy regulations and facilitate secure data exchanges. IDC notes that advances in generative AI have enhanced the accuracy of synthetic data, which makes it a valuable tool.

“Across various sectors, AI is revolutionizing processes and driving transformative changes. In the insurance industry, AI adoption is reshaping operations, from enhancing risk assessment with synthetic data to optimizing sales through generative AI-based tools,” Surya Narayan Saha, Research Manager, Financial Insights, IDC Asia/Pacific, said in a release.

The Power of AI Upskilling in insurance: Skills we need to develop

There is a high demand for specialists and experts who can implement, integrate, maintain, govern and develop AI applications – and, importantly, create the foundation architecture for it to work. Data engineers, AI architects, data scientists, Machine Learning engineers are not just sought after in the insurance industry.

AI enhances capabilities of knowledge workers

At the same time, AI is changing the roles of technical experts, as we have observed at Swiss Re through our experimentation with code completion and pair programmer tools. These tools offer suggestions for code completion or alternatives and can even write code autonomously. However, the engineer remains in control, with the ability to accept or reject the suggestions. The results have been remarkable, as the tool has already demonstrated productivity increases of up to 20% among our colleagues using it. The time saved can be allocated to increased creativity and to do tasks that often fall to the bottom of the "to do" list, such as mentoring colleagues, learning and knowledge sharing.

Specialists are just the tip of the iceberg when it comes to people who need to have Gen AI skills. While AI can automate certain tasks, a significant portion of its value is derived from its integration into everyday tools, such as emails, document production and meeting admin. This helps us all increase productivity and enable us to focus on tasks we enjoy and create greater value add.complete report