Artificial intelligence in risk management: explainability and causality as key pillars

What it is about

How artificial intelligence is transforming risk management, the importance of explainable AI, and the implementation of causal models for decision-making in complex and regulated environments.

The webinar reviews the limitations and inconsistencies of widely used explanation methods, such as SHAP and LIME values, with examples of common applications, and applies causal models and explanation methods that incorporate the explicit causality of the variables.

It includes a case study in which explainability and causality were key to solving challenges in the financial sector, and shows how these tools can be used to anticipate and mitigate risks.