Climate change induced by greenhouse gas (GHG) emissions poses a serious threat to the planet. As a result, it has become one of the main topics of discussion among governments and financial regulators due to its potential to slow economic growth, reduce employment and undermine the stability of financial markets.[1] Central banks and regulators across the world are taking steps to ensure that banks are prepared for any shocks to the financial system by having significant financial institutions stress test their counterparty exposures and portfolios under a range of different climate scenarios.
Members of the climate team at this large global bank are responsible for measuring and managing the bank’s exposure to environmental threats. This includes undertaking stress tests of the bank’s loan portfolios in order to meet regulatory requirements being driven by the European Central Bank (ECB) and the Monetary Authority of Singapore (MAS). These exercises require access to robust climate data and analytics to effectively model transition paths to net zero for the bank’s corporate clients.
Pain Points
The bank wanted to select a sophisticated approach to meet regulatory requirements for stress testing its loan portfolios, plus build a top-quality climate database to support related initiatives across the bank.
The climate team needed to stress test the bank’s loan portfolios to uncover any potential vulnerabilities related to financed emissions. At the same time, a number of groups throughout the bank wanted to build a robust climate database for firm-wide access to support a broader range of risk-related issues. Both groups needed high-quality data for their initiatives, and the climate team took the lead on identifying a well-recognized firm that could provide:
- A single source of broad and reliable environmental data for public companies.
- Estimated emissions for small- and medium-sized enterprises (SMEs), where this information is typically not reported.
- The ability to run an analysis on a large number of companies within a portfolio.
- A sound methodology to evaluate transition paths to net zero under different climate scenarios.
- Capabilities to easily link internal and external data that use different classification schemes to have one unified database.
- Efficient data access options to support analysis and the development of a company-wide data warehouse.
The team began discussions with S&P Global Market Intelligence (“Market Intelligence”) to learn more about the firm’s offering.
The Solution
Market Intelligence began by discussing Climate Credit Analytics, a climate stress‑testing framework and scenario analysis model suite, which was launched in 2021. Climate Credit Analytics makes the critical link between climate change and credit risk by translating climate scenarios into drivers of financial performance (e.g., production volumes, fuel costs and capex spending) tailored to specific industries. These drivers are then used to forecast complete company financial statements under various climate scenarios, including those published by the Network for Greening the Financial System (NGFS), a group of over 120 central banks, financial authorities and observers.[2]
Developed through a collaboration between Market Intelligence and Oliver Wyman,[3] Climate Credit Analytics includes an automated capability to evaluate more than 1.6[4] million public and private companies, as well as the ability for users to input proprietary information to expand this analysis. The solution covers five carbon-intensive sectors (Airlines, Automotive, Metal & Mining, Oil & Gas and Power Generation) and also provides a generalized approach for all other sectors to complete the portfolio analysis.
Climate Credit Analytics leverages Market Intelligence’s proprietary datasets and capabilities, including financial and industry-specific data, sophisticated quantitative credit scoring methodologies and company-level data from Trucost, the data and analytics engine that powers many of S&P Global’s ESG solutions. These capabilities would enable users at the bank to:
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Conduct climate scenario analysis and stress loan portfolios | Climate Credit Analytics translates climate scenarios into scenario-adjusted financials and creates credit scores[5] at the company level. The solution enables climate scenario analysis through 2050 by natively incorporating the 2021 scenarios published by NGFS and evaluating both climate-related risks and opportunities. It also supports the evaluation of user-defined scenario values. |
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Embed differentiated data |
Climate Credit Analytics automatically extracts relevant company financials, borrower-level credit scores and industry-specific data from Market Intelligence to support a bottoms-up modeling approach. This includes: S&P Capital IQ Premium Financials that provides standardized data for over 5,000 financial, supplemental and industry-specific data items for over 150,000 companies globally Private Company Data that covers more than 10 million private companies with financial statements, enabling users to utilize these datapoints alongside any other proprietary information for the purpose of the analysis SNL Energy that covers more than 9,000 power plants, 3,000 North American energy companies, 1,700 active coal mines and 120 gas pipelines. This includes details on financials, supply and demand fundamentals, hourly |
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Access cutting-edge analytics |
Credit Analytics blends cutting-edge models with robust data to help users reliably assess the credit risk of rated and unrated, public and private companies across the globe. |
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Easily integrate internal and external data |
Business Entity Cross Reference Services quickly links the standardized and proprietary IDs for over 28 million entities to the primary key, the S&P Capital IQ Company ID. |
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Deliver data to a centralized warehouse |
The robust Capital IQ Pro desktop is complemented by an API solution that enables clients to receive enterprise-level data securely through RESTful APIs, drawing upon fewer resources than typically required for traditional data feeds. |
Key Benefits
Members of the climate team were impressed with the combination of Market Intelligence’s data resources and credit analytics and Oliver Wyman’s climate scenario and stress-testing expertise. The team’s colleagues in other areas of the bank also saw immense value in having one well-respected provider of an extensive set of environmental capabilities. A decision was made to utilize the Climate Credit Analytics offering to provide the bank with:
- A methodology to translate complex climate scenarios into drivers of financial performance to carry out stress tests, including sophisticated quantitative credit scoring.
- Comprehensive and consistent sector-specific modelling covering key high carbon-emitting sectors, plus others.
- A solution that leverages Market Intelligence’s proprietary datasets, including renowned financials and environmental information.
- Ongoing support to thoroughly understand the underlying data and methodologies.
- Improved workflows with easy integration of internal and external data using cross-referencing capabilities.
- Access to a robust desktop solution with tech-forward productivity tools, plus feeds for seamless data delivery to an internal system paired with an API for interactive forecasting and scoring.
Click here to explore some of the datasets and solutions used in this case study.
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[1] “Q&A Credit Risk Perspectives Series: Navigating Climate Scenario Analysis”, S&P Global Market Intelligence, May 5, 2020, www.spglobal.com/marketintelligence/en/news-insights/blog/qa-credit-risk-perspectives-series-navigating-climate-scenario-analysis.
[2] NGFS, as of February 14, 2022, www.ngfs.net/en/about-us/governance/origin-and-purpose.
[3] Oliver Wyman is a third-party consulting firm and is not affiliated with S&P Global or any of its divisions.
[4] All coverage numbers as of December 2021.
[5] S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by Market Intelligence. Lowercase nomenclature is used to differentiate Market Intelligence credit model scores from the credit ratings issued by S&P Global Ratings.