EAA Web Session: "GenAI Beyond the Basics: Advanced Concepts for Actuaries"

Einleitung/Dauer

Generative AI (GenAI) tools, such as ChatGPT, are rapidly reshaping the way actuaries approach problem-solving, analysis, and communication. Beyond their familiar web-based interfaces, these tools offer powerful functionalities through programmatic integration, which allows users to directly access and interact with the underlying Large Language Models (LLMs) via APIs (Application Programming Interfaces). Unlike standard web-based interactions, API access enables actuaries to seamlessly integrate GenAI into their existing workflows and scale usage efficiently to handle larger volumes. In this two-hour web session, participants will experience live demonstrations of advanced GenAI concepts through a Jupyter notebook that presents each concept and applies it to actuarial use cases; the notebook will be shared with attendees to encourage experimentation and support adoption in their own actuarial workflows.

After introducing the basics of using LLMs through APIs, we will explore the following advanced GenAI concepts:

• Structured Outputs: Generating responses in structured formats like JSON to support easier and more reliable downstream processing.
• Function Calling: Enabling LLMs to execute predefined functions, such as calculations or database queries, to perform specific operations.
• Fine-Tuning: Customizing pretrained LLMs with domain-specific data to improve accuracy and relevance in generating responses.
• Retrieval-Augmented Generation (RAG): Combining LLMs with external data sources to produce contextually enriched outputs.

For each concept, the session will cover its purpose, underlying principles, and functionality, illustrated through a dedicated actuarial use case and complemented by further applications and resources. It will conclude with a forward-looking outlook on emerging developments, followed by an open Q&A and discussion.

Vorgehensweise und Ziele

The aim of this web session is to equip participants with the knowledge and practical skills needed to apply advanced GenAI concepts in actuarial contexts. By the end of the session, participants will be able to:

• Understand how to interact with GenAI tools programmatically through APIs.
• Customize LLMs using fine-tuning techniques to address domain-specific requirements.
• Combine external data with LLMs through Retrieval-Augmented Generation to produce contextually relevant outputs.
• Leverage Function Calling capabilities to trigger the execution of predefined functions in the underlying programming language, such as calculations or database queries.
• Generate structured outputs in JSON format to ensure compatibility of the responses with actuarial processes and systems.
• Apply Python programming skills to develop and integrate GenAI-powered solutions into actuarial workflows.
  

The session strikes a balance between conceptual understanding and hands-on programming, providing participants with tools to enhance the accuracy, efficiency, and creativity of their actuarial work.

Teilnehmer

This web session is designed for actuaries, data scientists, and other professionals in the insurance and financial industries who wish to deepen their technical understanding of GenAI tools. It offers practical insights for those new to programming and GenAI, while also enabling experienced users to refine their skills and explore advanced techniques.

A basic familiarity with Python (or another modern programming language) is helpful but not required. The session provides clear guidance and hands-on examples to ensure accessibility for all participants.

Technical Requirements
Please check with your IT department if your firewall and computer settings support web session participation (the programme Zoom will be used for this online training). Please also make sure to join the web session with a stable internet connection.

Dozierende

Dr Simon Hatzesberger
Simon Hatzesberger is an actuary working as a Manager in Actuarial & Insurance Services at Deloitte. During his previous tenure in the actuarial department at Allianz Private Health, he was responsible for various data- and AI-related topics for several years. He holds an MSc degree in Financial Mathematics and Actuarial Sciences from the Technical University of Munich, as well as an MSc degree in Computer Science and a PhD in Mathematical Stochastics from the University of Passau. Additionally, he is a member of the German Association of Actuaries, a Certified Actuarial Data Scientist, and a Certified Enterprise Risk Actuary. He is actively involved in several Actuarial Data Science committees of the German Association of Actuaries, serves as a workstream lead in the Artificial Intelligence Task Force of the International Association of Actuaries, and is a member of EIOPA’s Consultative Expert Group on Data Use in Insurance.

Sprache/Kurztitel

The language of the web session will be English.