From Text to Action:
How LLMs transform insurance companies

The arrival of generative AI and large language models (LLMs) is revolutionising the way we interact with technology. Not only personally but professionally: these models, able to understand and generate human language with astonishing accuracy, are opening new paths for improving and make work more efficient.

In the Insurance Industry, the applications of AI and LLMs extend to multiple aspects, offering unprecedented opportunities for innovation and operational efficiency. One of these aspects is the capacity of LLMs to process and generate text from large volumes of data with the aim of analysing the information and generating quality reports. On the other hand, the automation of claims management through AI allows to process them quickly and accurately, increasing customer satisfaction and optimising the company’s resources. These applications are just the tip of the iceberg, since the flexibility of LLMs enables them to adapt to the tasks and challenges of the sector.   

For instance, in customer service, chatbots, although already in use, can provide instant, more tailored, and specific responses to users’ queries when powered by LLMs, thus improving user experience while reducing operating costs. This technology applies to automated mailing and face-to-face interaction with customers, among others, where the insurer’s interlocutor will have more knowledge and tools for offering the best service.  

However, all that glitters is not gold. Do not jump on the bandwagon without first understanding the needs of these solutions in order to exploit their full potential. The successful implementation of LLM models in insurance companies requires a robust and specialised methodology such as MLOps. This methodology provides an operating framework which covers the entire cycle, from development to the deployment and monitoring of models, ensuring that they are efficient, scalable, and aligned with business goals. In the Insurance Industry where accuracy and service personalisation are fundamental, these practices allow to make the most of the capabilities of LLMs.   

But MLOps is not the only requirement for the adoption of LLMs: a culture open to new technologies and ethical governance of their possibilities are essential. This need arises from the sophistication of the models, their complexity, training and retraining, and the intensive demand for computational resources.   

In this dynamic scenario the Insurance Industry is in, the integration of technologies such as LLMs and generative AI is no longer an option, but a strategic need. For this reason, it is essential to find a travel partner who has a deep understanding of the industry and the ability to implement AI across the value chain. This partner must be able to build these collaborative environments adapted to the technological environments. To this end, it is essential that it invests in quality human capital with multidisciplinary teams.