adesso BLOG

This is the first part of my three-part blog post on the role of artificial intelligence (AI) in the insurance industry. In this first part, I focus on the general opportunities and challenges of AI as well as specific applications of generative AI. The focus is on marketing and sales.

Everyone makes GenAI and that's right! But despite the hype surrounding GenAI, the fundamental field of machine learning (ML) must not be forgotten. Because there is still a lot of untapped potential for companies here. Few people understand what artificial intelligence (AI) actually includes and how to use it optimally for their own business. We have gained a lot of experience as trainers and based on this we have created suitable training offers.

In a world where technology is advancing relentlessly, the various insurance intermediary professions need to adapt the way they work in order to stay relevant. But how do insurance intermediaries feel about this new technology? I answer this question in my blog post.

One of the major global challenges is the climate crisis. It has been with us for some time now and is manifested, among other things, in the increase in climate-related extreme weather events. AI can also provide support in this context. This blog post shows in which scenarios this can be the case, how the use of AI works and what hurdles there are.

AI is indispensable for companies' customer relationships in the digital world. It automates processes, analyses data intelligently and adapts content in real time. This enables customer needs to be better met, customer behaviour to be analysed in detail and all touchpoints of the customer journey to be optimised. I present further insights and solutions in my blog post.

The world of artificial intelligence (AI) has evolved rapidly with the introduction of generative models such as ChatGPT. This blog post takes a look at use cases for generative AI and considers the future possibilities of this technology.

Large Language Models (LLMs), above all ChatGPT, have taken all areas of computer science by storm over the past year. As they are trained on a broad database, LLMs are fundamentally application-agnostic. Despite their extensive knowledge, however, they have gaps, particularly in highly specialised applications, which in the worst case can only appear to be compensated for by hallucinations. To reduce this risk, "Retrieval Augmented Generation" (RAG) has been established.

The degree of digitalisation in facultative business is still significantly lower than in other areas of the company, even among the largest reinsurers. The reason lies in the nature of the business: it works with few, highly individualised contracts - but business processes that work with a high degree of standardisation and large volumes are best digitised. With the latest advances in the field of artificial intelligence (AI), the tide is turning: new solutions can be a game changer to increase the efficiency and quality of facultative underwriting and make data available for other applications.

When searching for relevant sources for editorial work, specialised knowledge databases are indispensable. But even in this rich pool, the search is often laborious. The solution? Concise summaries of each entry that enable a quick assessment of relevance. In our blog post, we present proven concepts for the mass generation of machine-generated summaries with Aleph Alpha Luminous and report on a realised customer project.