Discover how Natural Language Processing, Large Language Models, and Generative AI can transform actuarial work. This hands-on course explores foundational concepts, real-world use cases, and strategic implementation techniques. Designed for insurance professionals, it provides practical tools to integrate AI into workflows, enabling innovation in underwriting, modelling, and beyond.
If you’re looking to learn more about how to apply NLP and GenAI (LLM) techniques to actuarial work, this course is ideal for you. An individual subscription gives you 3 months’ online access to:
As Well As
Our AI, Industry and Actuartech Resource Libraries which feature curated additional content to assist you on your data science journey.
A coding project is also available to practice the skills you learn in this course.
This course is designed to introduce NLP, LLM and Gen AI in the context of insurance; with practical use cases and hands-on examples. The course will cover an overview of the theory, example practical applications as well as an overview of the broader ecosystem in which these techniques could be utilised.
Participants will embark on a journey from understanding the basics of what NLP and LLMs are, including an overview, specifications, evaluations, and their pros and cons, to starting to explore how to effectively utilise these techniques through various techniques such as prompting, and fine-tuning strategies.
We cover example strategic implementation, featuring case studies and real-world challenges to equip attendees with the knowledge to initiate their own projects or integrate NLP or LLMs into existing workflows.
By the end of course, participants will have been provided with the foundation content of the operational and strategic aspects of NLPs and LLMs which will help to enable participants to harness these powerful tools for a wide range of applications in insurance and risk management.
Chapter 0 introduces the course and provides and overview of what to expect.
Chapter 1 dives into extracting valuable insights from text data, focusing on core techniques for preparing and analysing raw text.
Chapter 2 explores how words and phrases are represented as vectors, delving into various embedding methods and their role in understanding language.
Chapter 3 introduces key tasks in natural language processing, from understanding meaning and context to generating useful outputs like summaries or classifications.
Chapter 4 provides an overview of generative AI, particularly large language models, examining their capabilities, customisation, and real-world applications.
Chapter 5 discusses the challenges and limitations of NLP and LLM technologies, along with best practices for developing and deploying AI systems responsibly.
We have tailored packages available to ensure that corporate teams have the option to attend structured live lessons by our tutors, and the option to request a practical assignment and bespoke feedback. Invoicing option available.
