PAW Climate Expert Round 1: Climate Change & Risks

date:

Monday, Jun 14, 2021

Time:

9:10 am

Track:

Summary:

  1. Introduction to Climate/Sustainable Finance & Its Link with AI (Pedro Baiz)

    The Financial Stability Board (FSB) after working on regulations to address the 2008/9 financial crisis, moved to the next biggest risk: Climate Change. The talk will provide a comprehensive overview of what constitutes Climate/Sustainable Finance. Standards and potential upcoming regulations, such as TCFD (Task Force on Climate related Financial Disclosures) among many others (e.g. SASB, GRI, IRFS, etc) will be covered. Finally, the role of AI and on this emerging field will be discussed.

  2. Predictive Analytics for Climate Risk Assessment (Christian Spindler)

    Climate change is a systemic risk which impacts on all business sectors. It increases uncertainty and investment risk and endangers entire business models. Professional investors and asset managers are taking climate change more and more into account. Corporates are starting to quantify climate risk in their mid- and long-term strategies. Predictive analytics turns out to be key in making quantified assessments in mostly unexplored terrain: How are extreme weather risks impacting on production sites and physical assets of the firm? How is the upcoming carbon taxation impacting on the company now and in future? And how vulnerable is the global supply chain of the company against business interruption risks? This presentation explores methods and tools for climate risk quantification.

  3. Sustainable Federated Learning for Predicting Climate Changes (Sharmistha Chatterjee) 

    The talk unveils the art of building sustainable federated machine learning models by considering different aspects of ethical AI. The talk will highlight various techniques of incorporating fairness in private federated learning while ensuring the sustainability of future smart ecosystems. By the end of the talk, the audience will get the know-how of sustainable federated learning, how to build a scalable distributed architecture, and important KPIs to focus on when designing such a system.

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