AI in insurance - where it really makes a difference

AI is a hot topic these days, and those not yet using AI are keen to get started as soon as possible. But for those who have already tested various AI solutions, some may have experienced a few disappointments: Despite the promises of revolutionary change, breakthroughs are still rare. However, there are specific areas, especially in the insurance sector, where AI can be particularly effective. At Intero Consulting, we are working extensively on this topic and have gathered valuable insights that will make a (new) start with AI easier. 

Insurance companies have their own extensive databases, positioning them well to benefit from AI. However, the challenge lies in efficiently processing these large data volumes to maximise the benefits. To successfully leverage AI in the insurance sector, several specific considerations come into play:

• A unique feature of the insurance industry is that it often involves handling very sensitive data that is restricted to internal use. AI models must therefore be trained in-house, which requires expert knowledge.
• The EU AI Act sets out specific guidelines for the insurance sector and prohibits the use of certain AI applications, such as social scoring, in order to regulate the use of particularly critical technologies.
• Traceable and transparent results are particularly important for working with this type of data; highlighting the importance of Explainable AI (XAI) as a key requirement.

To facilitate employees getting started with Large Language Models (LLMs), prompt libraries can serve as a highly effective tool. A prompt library is a collection of predefined prompts that cover a wide range of use cases while incorporating best practices. Prompt libraries ensure consistency in AI-produced results and contribute to improved AI performance by using optimised prompts. They also provide a centralised repository of prompts accessible throughout an organisation, allowing a range of departments to access the same AI inputs. This simplifies the integration of AI into different areas and promotes sustainable optimisation.

In conclusion, the effective use of AI requires a strategic approach and solid foundations: Companies must not only keep a close eye on the market but also identify specific use cases where AI can be used effectively for optimisation purposes. Robust data management is also a key prerequisite, as the quality and quantity of data have a significant impact on the performance of AI. With our extensive experience in setting up such data management systems, we provide consulting support to our clients, helping them unlock the full potential of AI within their organisations.