{"id":17422,"date":"2024-02-29T12:05:54","date_gmt":"2024-02-29T09:05:54","guid":{"rendered":"https:\/\/insly.com\/?p=17422"},"modified":"2024-10-31T16:32:16","modified_gmt":"2024-10-31T13:32:16","slug":"generative-ai-in-insurance-building-a-long-term-strategy","status":"publish","type":"post","link":"https:\/\/insly.com\/en\/blog\/generative-ai-in-insurance-building-a-long-term-strategy\/","title":{"rendered":"Generative AI in insurance: Building a long-term strategy"},"content":{"rendered":"
<\/div>

Generative AI has the power to transform insurance, but companies must consider their strategy carefully to navigate data protection hurdles, build a trusted AI model, and create a culture of collaboration between AI talent and insurance experts.\u00a0<\/span><\/em><\/p>\n

Since ChatGPT burst onto the scene in 2022, thought leaders and commentators have gone into overdrive about the potential use cases for large language models (LLMs) and generative AI in insurance. There\u2019s no shortage of predictions on what an AI-driven insurance future will look like, how the technology <\/span>could revolutionise underwriting<\/span><\/a> or claims, and whether it will eventually replace human insurance professionals.\u00a0<\/span><\/p>\n

In its white paper, <\/span>Insurance 2030<\/span><\/a>, McKinsey predicted that just six years from now, AI will be deeply embedded in the insurance sector, spanning claims, distribution, underwriting and pricing. But for many insurance firms, who are still early in their digital transformation journeys, or relying on legacy tech and manual processes, these discussions may seem premature.\u00a0<\/span><\/p>\n

The truth is that, as with any new technology, the hype of generative AI is still outpacing the reality. The technology was placed at the peak of <\/span>Gartner\u2019s Hype Cycle 2023<\/span><\/a>, and we are still a long way from maximising its full potential in insurance. Moving from predictions to practically implementing generative AI and LLMs throughout the insurance lifecycle will involve overcoming numerous hurdles and rewiring how the insurance sector operates from top to bottom. As McKinsey outlines in its report, the possibilities are huge, but it won\u2019t happen overnight.\u00a0<\/span><\/p>\n

Here, we dig beneath the hype to understand what is involved in implementing AI in insurance and making an AI-powered insurance company a reality.<\/span><\/p>\n

Is generative AI a game-changer for insurance?\u00a0<\/span><\/strong><\/h4>\n

AI is a broad term, which spans a host of different systems, from <\/span>machine learning<\/span><\/a> and table data through to the most complex generative AI and LLM systems like ChatGPT-4. The reason LLMs have generated so much hype is that they are extremely adaptable, with the capacity to cope with a wide variety of tasks including creating text and images or writing code – doing so almost instantly – based on simple instructions.\u00a0<\/span><\/p>\n

The speed at which LLMs can complete tasks is what makes them so impressive. Experience them in action and it seems perfectly plausible that ChatGPT-style systems could eventually carry out numerous insurance tasks, or even run an entire insurance business. But, moving from specific use cases to full-scale roll-out will involve overcoming some significant challenges.\u00a0<\/span><\/p>\n

Implementing generative AI in insurance companies \u2013 the reality<\/span><\/strong><\/h4>\n

Managing data protection risks\u00a0<\/b><\/h4>\n

As McKinsey rightly points out in <\/span>its report<\/span><\/a>, an AI programme is only as good as the data it is trained on. But the idea of companies using customer data or IP for such purposes raises alarm bells. Insurance companies are subject to strict data protection laws, which mean that integrating public generative AI tools such as ChatGPT into insurance workflows is a no-go, and there should be caution around letting employees use the tools for their work. Research found that <\/span>11% of the data fed into ChatGPT<\/span><\/a> is confidential and numerous companies including JP Morgan and Verizon have blocked ChatGPT due to risk that IP or sensitive data will leak out.\u00a0<\/span><\/p>\n

Building a closed model\u00a0<\/b><\/h4>\n

To let an LLM algorithm loose on customer data and company IP, a closed system is needed, where the system is trained on specific data, with no danger that it could be accessed by a third party. A closed system is designed to suit a company\u2019s specific requirements and data set and should be constantly updated as those needs and data evolve. Companies can build closed LLMs by fine-tuning existing base models and, in basic terms, there are three steps involved:<\/span><\/p>\n