Episode 25: The Risks of Applying Data Science to Financial Modelling
About this episode
Pretty much everyone has a retirement plan, but those plans aren’t always robust enough to see you through to the finish line of life. And part of that is a direct consequence of incorrectly applying data science principles to financial modelling.
In this episode, Todd Tresidder joins Dr Genevieve Hayes to discuss the risks and limitations of using data science when planning for retirement.
Guest Bio
Todd Tresidder is a former hedge fund manager who “retired” at age 35 to become a financial consumer advocate and money coach. He now runs the popular retirement planning website FinancialMentor.com and is the author of a range of books on retirement planning and investments including How Much Money Do I Need to Retire? and The Leverage Equation.
Talking Points
- What are some of the limitations of traditional financial modelling?
- Examples of what can happen when traditional financial modelling goes very wrong.
- How to do financial modelling the right way.
- The Engineer’s Fallacy or why you shouldn’t apply pure data science to financial planning.
- The implications of this for fields outside of the financial services industry.
Links
- Todd’s Website
- Connect with Genevieve on LinkedIn
- Be among the first to hear about the release of each new podcast episode by signing up HERE
Get every episode summarized
Each time Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.
Email me new episodesFree for 3 shows. No card needed.
Hosts & guests
No transcript yet
This episode has not been transcribed. Request it and it moves to the front of the queue.
More episodes
More from Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
Episode 100: What Data Science Value Really Means
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
Episode 99: [Value Boost] Preventing ML Bias Before it Becomes a Problem
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
Episode 98: Building Trust in AI Through Model Interpretability
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
Episode 97: [Value Boost] Mathematical Modelling as a Gateway to ML Success
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.