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Simona Paravani on AI in Finance: An Overview

With introductions by Markus Brunnermeier
December 20, 2024
12:30 pm
Markus' Academy

More from this series
Online: Zoom

Video & Timestamps

On Friday, December 20, Simona Paravani joined Markus’ Academy for a conversation on “AI in Finance: An Overview.” Simona Paravani is the Global CIO of Solutions within BlackRock’s Multi-Asset Strategies and Solutions group as well as a Supernumerary Fellow; Distinguished Affiliated Professor, Faculty of Economics at the University of Cambridge.

We apologize that due to technical difficulties, we could not show the webinar live on 2024-12-19 and had to record it separately on 2024-12-20.

Watch the full presentation below. You can watch all Markus’ Academy webinars on our Markus’ Academy YouTube Channel.

Timestamps:

[0:00] Markus’ introduction

[08:37] AI’s macro and microeconomic impact

[25:09] Asset management and AI

[32:15] AI biases and regulation

Executive Summary

A few highlights from the discussion.

  • Summary in four bullet points:
    • AI has progressed rapidly due to advances in computational power and data availability, growing from 2 to over 100 zettabytes in 15 years. It is here to stay, driven by the continued growth of data, favorable attitudes among younger generations, and its demonstrated productivity gains
    • AI will improve the ability of the asset management industry to deliver investment insights by processing unstructured and real-time data, enriching the pool of data beyond official statistics or broad surveys
    • While the focus on how to best use AI has remained on prompt engineering, the long-term productivity gains will depend on its users’ critical thinking, interdisciplinary collaboration, and teamwork. This skills challenge does not receive enough attention in the public debate
    • AI regulation should remain technology-neutral, ensure inclusivity, and address biases in training data. These biases arise from challenges like the digital divide and the failure to ensure technologies are evaluated with input from a representative sample of end users
  • Click here for the full summary.