AI is revolutionizing the insurance world: data power instead of brand names!

AI is revolutionizing the insurance world: data power instead of brand names!

The integration of artificial intelligence (AI) into the insurance industry is increasingly becoming a crucial factor for success. According to that Insurance Monitor AI technologies are changing the way customers search for information online. Insurance companies face the challenge of optimizing their content for both traditional search engines and language model-based systems.

A central element here is the readability and structure of the data. It becomes clear that it is not so much the size of an insurer's internet presence that is important, but rather how well the information is prepared for AI systems. These findings come from a white paper from the Ergo Innovation Lab in collaboration with the consultant Ecodynamics.

The role of large language models

An important aspect in this context are the so-called Large Language Models (LLMs), such as ChatGPT. These powerful models for processing and generating human language have a wide range of applications in companies. These include natural language processing, content creation, customer support through chatbots, as well as sentiment analysis and information retrieval. How Fraunhofer IESE reported, many LLMs often do not require additional fine-tuning for various tasks, which makes their implementation easier.

When choosing a suitable LLM, it is important to consider the specific needs of the company. Important factors in this selection include adaptability, technical compatibility, cost, and legal and ethical implications. A structured approach to implementation includes, among other things, the definition of tasks, the assessment of computing capacities and the identification of the data to be integrated.

Important considerations when choosing an LLM

Additionally, there are eight key points that should be considered when selecting an LLM:

• Sprachliche Kompetenz
• Qualität, Vielfalt und Größe der Trainingsdaten
• Anpassungsfähigkeit (Finetuning)
• Modellgröße und Rechenleistungsanforderungen
• Kosten und Budget
• Datenschutz und Sicherheitsvorschriften
• Umgang mit Verzerrungen
• Transparenz und Erklärbarkeit

A long-term perspective recommends checking whether an LLM is actually necessary or whether smaller models could be sufficient. One of the limitations of LLMs is the possibility of “hallucinations,” or false outputs. Due to these challenges, it is recommended to leverage expertise from an AI Innovation Lab to effectively support the selection and implementation of AI language models.

The changes brought about by AI in the insurance industry are both a challenge and an opportunity. Insurers ready to embrace these developments can benefit significantly from optimizing their data and integrating LLMs.