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Banking transformation for a climate-resilient future

Learn how banks use climate risk scenarios to assess their vulnerability and financial impact from climate-related risks.

Climate scenario analysis: Challenges and considerations for banks

July 31 marked a milestone for how financial institutions will account for climate risks in lending. That was the day that the results of the Fed’s Climate Scenario Analysis Pilot were released. As the results are being assessed, other US regulators have come forward with similar climate risk management proposals. A key element of these proposals is climate scenario analysis, which assesses the impact of climate on key variables such as gross domestic product (GDP), unemployment, inflation, and how changes in those variables affect the borrowers’ risk parameters. In this blog, we explain what factors banks need to consider and the challenges they face in implementing these difficult analyses.

A climate scenario analysis is separate from the current stress tests being performed by banks, which use existing stress testing framework where possible for climate risk quantification. But they need to address additional climate-specific challenges in data, modeling, and reporting.

To quantify climate-related risks across the entire business, banks will require loan-level data at a much more granular level than used in the stress tests. They will need to classify subsegments of their loan portfolios according to their degree of exposure to physical and transition risk. On top of building their own capacity to perform a climate scenario analysis, the exercise requires counterparty data that may not always be easily available or of consistent quality, especially for smaller, private counterparties where detailed information—particularly climate-related information—is not available.

Currently, there are three options where banks can use stress test data to create climate scenarios and assess their vulnerability and financial impact against climate-related risks: (1) the Network of Central Banks and Supervisors for Greening the Financial System (NGFS),1 (2) third-party vendors, and (3) the bank’s own in-house development. The following table summarizes the pros and cons of each scenario sourcing option:

Scenario typeProsCons
Network of Central Banks and Supervisors for Greening the Financial System(NGFS)
  • The NGFS provides a set of six scenarios that are classified according to policy targets, policy reactions, the level of technological change, the amount of carbon dioxide removal, and the amount of variation in country policies; while shared with different realms, the NGFS scenarios are considered to provide consistent results to IEA or IPCC scenarios.
  • Multiple global central banks have released stress tests using the NGFS scenarios as their foundation (i.e., the NGFS has become a global standard bearer for climate risk assessment).
  • The NGFS scenarios are available to the public.
  • The latest vintage of NGFS scenarios (Phase III, 2022) include an expanded set of macroeconomic variables.
  • Only national or regional level climate scenarios are available (i.e., it requires downscaling to make it useful at the bank level).
  • The NGFS uses the National Institute Global Econometric Model, which did not seem to account for a recovery period following the COVID-19 recession.
  • The GDP pathways under the NGFS's downscaled national/regional data do not include the impacts of physical climate risks (i.e., the impact of chronic physical shocks that are expected to result from climate change, including the gradual erosion of productive capacity as temperatures rise).
  • Unlike chronic physical risk, acute physical risk estimates are yet in primitive stage.2
Third-party vendor
  • Minimal effort is expected in downscaling compared to the other options.
  • Often better granularity and accuracy in data are provided.
  • Scenarios come at a cost.
  • Scenarios may not provide full transparency or accessibility due to proprietary issues, including for those assumptions and/or calculations used in generating estimated data for smaller entities.
Developed in-house
  • Scenarios will be customizable with full transparency and explainability.
  • Extensive resources and time are required to develop the scenarios.

Regardless of which sourcing options—or combinations of options—banks choose, customizing and reducing the scenarios by downscaling will be critical for carrying out meaningful climate scenario analysis. The choice of source(s) for the climate scenario analysis also needs to be based on the bank’s current profile and exposure to each type of climate-related risk.

To properly execute climate scenario analysis, the following steps need to be taken:

1. Perform a preparedness assessment:

Using emerging regulatory requirements and guidelines3 identify data elements necessary to support the analysis and reporting and the data sources for such data elements, as well as the lineage of the data elements from source to output and disclosure.

2. Establish strategic plans to fill in assessment gap:

This should include sourcing and procuring climate scenario pathways and resulting macroeconomic scenarios from external, third-party sources, as well as developing processes and procedures to onboard scenarios. Review scenarios in accordance with bank and regulatory requirements. In cases where the bank can develop its own scenarios in-house, a framework should be established to substantiate the developed scenarios against such requirements.

3. Establish the climate scenario analysis framework:

To build a rigorous framework, consider the following:

  • Develop processes and procedures to enable the bank to make modifications and/or adjustments to climate and macroeconomic scenarios supported by statistical, scientific, and economic leading practices.
  • Review existing loss forecasting models across portfolios to determine which models (or components) can be leveraged for climate risk forecasting with modification. If existing models cannot be relied upon as a starting point for climate risk modeling, then design and develop new climate risk forecasting models across the bank’s portfolios for both physical and transition risks.
  • Generate model business requirements, data extract, transform, and load (ETL) code, estimation code and logic, statistical analysis and support, forecasting code and logic, and model documentation.

KPMG supports banks and other financial institutions in their end-to-end climate risk quantification journey, starting with risk identification and exposure assessment, data and scenario sourcing, climate mediated credit-risk modeling, and communication of physical and transition risk impacts on their portfolios.

Footnotes

  1. There are also publicly available climate-related scenarios from the International Energy Agency (IEA) and the Intergovernmental Panel on Climate Change (IPCC) while their focus is not necessarily on financial sector.
  2. In the latest vintage (2022), estimates of global GDP impacts from acute physical risks were included.
  3. U.S. regulatory requirements and guidelines are still being developed as of this blog post, with the proposed SEC rules to be finalized and the Fed’s pilot Climate Scenario Analysis (CSA) exercise underway.

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Meet our team

Image of Adam Levy
Adam Levy
Principal, Modeling & Valuation, KPMG US
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Benjamin Harden
Advisory Managing Director, FS Risk, Regulatory & Compl, KPMG LLP

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