I recently attended the CeFPro environmental, social, and corporate governance (ESG) conference in London along with a variety of risk experts and ESG leaders from large global institutions. If you have followed my prior blog posts, you know that I have a keen interest in the topic of climate risk modeling and how it can help assess the economic impacts of climate change. The attendees at this conference all shared a common interest in the ESG topic with varying roles and responsibilities within their organizations. They were all there comparing notes and seeking input on their ESG initiatives, an arena where everyone is still learning.
In the various discussions I had over the two-day event, climate change appears to be the more clearly defined ESG initiative for most organizations. Alongside guidance initiatives from central banks such as the Bank of England’s pledge and the US Federal Reserve’s committees, organizations are assessing their strategy to measure risk and meet climate disclosure reporting requirements. The “S” [Social] and “G” [Governance] of ESG are also on their minds and definitely gaining traction. These aspects include topics such as financial inclusion, wage equity, diversity, and monitoring for bias in AI initiatives.
Specific to the environmental component of ESG, I observe that the oversight and responsibilities for financial services constituents can be placed into two broad categories:
Internal “Go Green” initiatives: This category includes the efforts of organizations within their own operation to reduce their carbon footprint and the direct accountability they hold to jurisdictions they operate within. You can imagine the challenge and opportunity for the physical footprint of banks and financial services providers globally. Ultimately, customers may choose providers according to their “green practices,” and stock valuations/profits will be impacted by an organization’s efforts.
Balance sheet management: The second category includes a firm’s external operations —their book of business and investments. This includes the customers serviced by the organization—businesses and consumers—for loans, asset management, transactional services, and so on. And a financial services provider typically services a wide range of industries that have their own commitments and expectations as it relates to climate initiatives. This is where the complexity comes into play with a multitude of interdependencies across many industries all touched by financial services. Firms are expected to understand their exposures, generate scenarios, and provide disclosures. I discussed the complex modeling considerations of physical, transition, and alignment risks in a prior post about climate risk models.
As expectations and regulations expand for disclosures on progress towards climate goals, it is critical that institutions have an accurate and flexible approach to understand their climate risk.
Climate Risk Measuring and Modeling
Simudyne is a partner of Cloudera that provides agent-based modeling (ABM) software. Justin Lyon, CEO of Simudyne, spoke at this same conference and I asked about his observations from the event and the state of readiness in the industry.
What third party data is needed to model climate risk?
Justin: The data chosen to calibrate the model is incredibly important, but you need to start with a model. Then determine your inputs and how you will source that data. That data can be produced with individual parameters in mind. It can represent what may happen if an aggressively conservative view is taken. For example, the rate of climate change occurs more quickly and has a greater and more immediate economic effect than expected—like an unanticipated shock.
But it is the model and its realism that matters initially. A small set of data points that are representative can be most insightful. I like to be guided by the phrase “make the important measurable, not the measurable important.”
What were some of your key observations from the discussions at the event?
Justin: Many of the conversations focused on the need for the right data, but I was struck that people are not nearly as far along as they would like to be in their strategy. They are struggling with running scenarios quickly with the right amount of data. They also need sufficient granularity to get a relevant and usable view for their client base.
If an institution already has Cloudera deployed, what are your recommendations on how they can use it to support agent-based models?
Justin: Cloudera Data Platform is incredibly powerful as an enterprise data platform. I would ensure institutions take advantage of Apache Spark to run their models very quickly on their existing Cloudera infrastructure. It can easily take data from a variety of third parties and map it into their models.
As it relates specifically to climate, firms can take the output of the geophysical and climate models they use and input those results into economic models. They can then translate those inputs into risk drivers, using our software, that affect their firms in particular.
Agent-based modeling can help predict the impact of climate change on a firm’s balance sheet. It enables robust scenario creation, improved risk management, and more efficient capital allocation. Cloudera and Simudyne are also working with Dell EMC. Leveraging Dell EMC PowerScale and running on Cloudera Data Platform, firms can execute agent-based modeling with greater efficiency. To learn more about ABM for risk modeling, read our agent-based modeling brief.