As the importance of environmental, social and governance (ESG) issues accelerates and boardrooms incorporate ESG into their strategy and investment decisions, financial services organizations will need to use data more intentionally to measure the success of their ESG reporting responsibilities and risk exposure mitigation efforts. This is especially important given that few industries gather more data than financial services.
Environmental data is currently tied to carbon emissions and energy usage; social data relates to metrics such as workforce diversity, gender equity and job creation; and governance data typically captures information on labor practices or anti-corruption policies. After accumulating this data, banks and other organizations in the financial services industry must use it to assess organizational risk and, ultimately, organizational value.
A significant majority of companies acknowledge that climate change is a threat to their business. About two-thirds of executives polled in RSM’s Middle Market Business Index survey on the topic of ESG say that—to at least some extent—climate change will lead to higher operating costs. And these percentages have remained fairly consistent year over year.
Growing regulatory and investor interest surrounding climate change and its impact on financial services organizations also puts more focus on data related to environmental issues, specifically. As a result, financial services firms must define, identify, collect, process, cleanse, analyze, model, interpret and report data to make well-informed decisions on green investing and ESG risk management.
Regulatory climate heats up
Since December 2021, the Office of the Comptroller of the Currency (OCC), the Federal Deposit Insurance Corporation (FDIC), and the Securities and Exchange Commission (SEC) have proposed guidance for assessing climate change-related risks and disclosing exposure to climate change, including for banks.
The OCC and FDIC draft principles largely focus on a bank’s efforts to provide its leadership team with the necessary guidance to create a governance and strategy framework for managing enterprise-wide climate change-related risks.
The SEC has proposed that registrants include in their public consolidated financial statements certain climate-related risk information, such as:
- The actual or likely material impacts of climate change-related risks on the organization’s strategy and business outlook
- The registrant’s governance of climate change-related risks and relevant risk management processes
- The registrant’s greenhouse gas emissions, which, for accelerated and large accelerated filers and with respect to certain emissions, would be subject to assurance
- Climate change-related financial metrics and disclosures in audited financial statements
- Information about climate-related targets and goals, and transition plan, if any
Once these rules are in place, action will be required.
Environmental and climate change data
Banks’ exposure to climate change can have significant effects on the business and the economy in which they operate. Physical risks such as floods, wildfires and hurricanes are of great concern; measuring such exposure is a priority when evaluating mortgage portfolios’ exposure to climate risk. Data on historical extreme weather conditions—used to predict future weather patterns’ effects on clients’ ability to pay and asset values—is therefore a high priority.
As the need for data grows, external providers have been stepping up to fill the data gaps. For example, the Task Force on Climate-Related Financial Disclosures (TCFD) recommends the use of carbon footprint metrics to uncover concentrated carbon risks. Bloomberg provides information and methods to calculate a portfolio’s footprint and offers comparisons to benchmarks or other portfolios. Moody’s provides information on physical risks with climate risk scores that quantify population-weighted exposure to floods, windstorms and other climate change elements.
To fully understand, assess and report on climate-related risks and metrics, financial services organizations such as banks also need internal data, and the form and breadth of that data will vary by organization. For instance, many banks have granular data on real estate flood risk; however, matching such data with internal data on its exposure to flood risk for buildings (used as mortgage collateral) has proven difficult as it requires substantial manual data cleaning, geographic information system or geocoding expertise, and human judgment. As a solution, banks can use artificial intelligence (AI) and machine learning (ML) to analyze, curate and model data from internal and external data sources to manage physical risks associated with climate change.
With the current focus on the “E” in ESG, leaders of financial services organizations should be asking themselves:
- Do we possess the tools and technology to collect, analyze, understand and report environmental data to meet stakeholder expectations and anticipated regulations?
- Do we have the technical knowledge and skills to perform the necessary scenario analyses and stress tests, present accurate disclosures in financial statements, and create internal policies to manage climate risk?
- Do we have appropriate ways to measure success and accountability?
Using the data
Once equipped with the appropriate data, financial services firms can begin to assess both the transition risk and physical risk of climate change. Those two risks are defined as follows:
- Transition risk refers to risks arising from the shifts in policy, consumer and business sentiment, or technologies associated with the changes necessary to limit climate change.
- Physical risk refers to harm to people and property arising from acute, climate-related events, such as hurricanes, wildfires and chronic shifts in climate, including higher average temperatures, changes in precipitation patterns and sea-level rise.
Stress testing and scenario analysis are not new and mainly use financial data. Financial services organizations will now need to determine how to incorporate nonfinancial data (i.e., physical risks and transition risks) of climate change into stress tests and analyses, which now require the consideration of multiple factors, risks or assumptions that are more complex than before.
Climate change-related risk exposures vary according to geographic location and composition of a bank’s loan portfolios or operations; therefore, understanding these exposures and climate risk drivers in their stress tests, scenario analyses and underwriting processes to estimate losses is important.
Although the SEC has mainly focused its proposal on environmental and climate change disclosures, organizations must also spend equal efforts on the social and governance efforts that fall under the umbrella of ESG. Social issues—the “S” in ESG—can relate to a company’s relationship with its internal and external stakeholders. For example, this might include matters that can affect a company’s profitability and reputational risk such as labor disputes or consumer backlash. Gathering data on employee sentiments and consumer opinions and preferences can aid financial services organizations in their social efforts.
The “G” in ESG is focused on how a company uses its governance model to drive accountability for its actions. Understanding governance risks is imperative for decision-making in daily activities or when facing a crisis. Some corporate scandals in recent memory have unveiled poor governance practices that have caused significant financial and reputational damage. In the face of companies’ missteps and expanding awareness of global diversity and income inequality, corporate governance is a core component of ESG.
Takeaways
As the regulatory environment surrounding climate change continues to evolve, financial services organizations will need to take a hard look internally at their operations, technologies and personnel to ensure they can comply with anticipated regulations. Changes will be needed to minimize rising regulatory risks, including determining how the relevant data needed for transparent reporting can be collected. The collection of the necessary data and its accuracy, completeness and reliability will be critical to that effort.
