Cross-border Tax Talks

Risk modeling: Quantitative insights for business intelligence

Doug McHoney (PwC International Tax Services Global Leader) is joined by Richard de Haan, PwC Global and US Risk Modeling Services Leader. Doug and Rich discuss climate risk modeling, the climate for risk in the C-Suite, how ESG is impacting business decisions, data sources (including geospatial data) for modeling, dealing with risk in the supply chain,the impact of AI and machine learning, and how visualizations help tell the story.

Timestamps:

• 2:15 - Rich has lived in over a dozen countries throughout his 30+ year career. What has he learned from these experiences?
• 5:10 - One of the main pillars of PwC’s Risk Modeling Services practice is climate risk modeling. With ESG being such a major topic globally, how are companies approaching climate risk modeling and how is it impacting their business decisions?
• 9:25 - Can you tell us more about the specific modeling within the broader climate risk modeling - physical risk modeling, transition risk modeling and greenhouse gas emission modeling? What are the sources of data for these models?
• 14:40 - With so many companies having mobile assets, how are you using geospatial data as part of your analyses?
• 16:40 - What are the C-suite’s perspective and interest in climate risk analysis as they look to invest in carbon neutral businesses?
• 20:35 - A combination of various factors, including the pandemic, has led to a major disruption in the supply chain, which often results in many tax implications. From a climate perspective, how are companies looking at key decisions with respect to supply chain?
• 25:00: In the international reporting space, there are new global proposals from OECD (Pillars One & Two) and increased ambiguity. From an ESG perspective, talk about how to manage the reporting side.
• 28:10: Outside of ESG modeling and climate risk modeling, tell me some more about corporate risk modeling and trust modeling.
• 30:35: Within tax, we’ve done quantitative modeling for decades, but we are focusing more on qualitative modeling. How do we make this process smart and enhance it with AI?
• 33:40: Outsourcing vs. insourcing: what is your general approach for companies doing this for risk modeling?
• 36:20: What underlying tools help create the single source of truth? What do you think about the tool choices both as an advisor and for companies?
• 40:40: Many companies want to own their own data in and data out. The challenge with these firewalls is that it’s hard to update when dynamic tax laws change. Do companies have the expertise to handle these changes?
• 42:25: In a corporate environment, we have evolved from large spreadsheets and moving into visualizations. Talk about the output and what is involved.

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