Overview
As artificial intelligence becomes increasingly embedded in investment workflows, distinguishing meaningful insights from unnecessary complexity has become an important challenge for investment leaders.
Join us for a timely discussion with Iro Tasitsiomi, PhD, an astrophysicist and investment data science leader, as she explores how to use AI and machine learning thoughtfully while preserving human judgment, transparency, and risk control.
In conversation with Frank Fabozzi, CFA, Tasitsiomi explains that sophisticated models add value only when paired with well-defined problems and high-quality data. Echoing Einstein, she argues that models should be made as simple as possible, but not simpler. She also shares when advanced machine learning techniques are most effective, particularly for capturing nonlinear relationships, working with unstructured data, or enabling unsupervised discovery.
Timings:
12:00 PM ET | 5:00 PM GMT
CFA Institute members may self-report one (1) Professional Learning (PL) credit for participating in this webinar.
Key takeaways
- Principle of parsimony: When the simplest model preserves essential insights and helps avoid unnecessary complexity, cost, and risk.
- Poke the model: Why probing model behavior, especially outside normal conditions, helps investment teams understand potential risks and failure modes.
- Understand the tools: Why sound judgment about data, assumptions, and model limitations matters more than coding skills, especially for younger analysts building credibility on AI-enabled investment teams.
- Speak the language: Why successful AI adoption requires both technical rigor and clear communication across investment teams.
Speakers
