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Capital Markets

AI Capital Markets Can Trust: Success Stories

Darko Matovski, PhD, Andre Franca, PhD, and Maxim Sipos, PhD, show how Causal AI unlocks superior decision-making in capital markets and present success stories.

Capital Markets participants can’t trust black-box machine learning that overfits to past data. 

Why do 87% of AI projects fail to make it beyond the experimental phase?

Darko, Andre and Max discuss the limitations of current approaches and share some cutting-edge success stories from causaLens clients. Andre and Max present a technical demo discussing execution strategy, portfolio optimization, and other exciting Causal AI use cases in Capital Markets.

Limitations of current approaches:

  • “Black-box” models can’t be scrutinized or trusted
  • Spurious correlations lead to severe overfitting
  • AI pipeline fails to capture human insight
  • Correlation-based models can’t optimize trading actions

Webinar sections

  • 00:00 – 09:36 Introduction to causaLens
  • 09:37 – 17:25 Introduction to Causal AI (Treatment Effect Estimation, Simpson’s Paradox, Causal Graphs, Causal Discovery, Confounders, Counterfactuals and Spurious Correlations)
  • 17:26 –  29:57 Causal AI in Trading & Execution (Linear vs Non-linear & ML vs Causal Models)
  • 29:58 – 44:35 Causal Portfolio Optimization
  • 44:36 – 51:06 Example Causal AI use-cases in Capital Markets: causaLake, Causal Model Risk Assessment and Stress Testing
  • 51:07 – 1:01:29 Q&A