blog Market Intelligence /marketintelligence/en/news-insights/blog/ratingsxpress-provides-the-data-a-bank-needs-to-develop-internal-ratings-based-models content esgSubNav
In This List
Case Study

RatingsXpress® Provides the Data a Bank Needs to Develop Internal Ratings-Based Models

Blog

Insight Weekly: Layoffs swell; energy efficiency PE deals defy downturn; 2023 global risk themes

Podcast

MediaTalk | Episode 30: US retailers prep for weaker online sales, holiday spending demand

Blog

Insight Weekly: Energy crisis cripples Europe; i-bank incomes rise; US holiday sales outlook

Blog

Japan M&A By the Numbers: Q3 2022


RatingsXpress® Provides the Data a Bank Needs to Develop Internal Ratings-Based Models

Highlights

The IRB approach is a bottom-up methodology and the credit risk team needed additional data to develop robust models that could gain regulatory approval.

In Europe, as elsewhere in the world, regulators have been taking steps to strengthen the resilience of the banking sector, so it is better positioned to absorb economic shocks. There has been a slight increase in capital requirements in Europe in 2022 − that is, the liquid capital that must be held relative to a certain level of bank assets. The European Central Bank (ECB) announced that overall capital requirements increased marginally to 15.1% of risk-weighted assets, up from 14.9% in 2021.[1] Banks meeting certain minimum conditions, disclosure requirements and approval from their national supervisor are allowed to use the internal ratings-based (IRB) approach to determine the capital required for various exposures.

This large European bank follows an Internal Ratings-based approach (IRB) to calculate minimum capital requirements. The credit risk modeling team is responsible for assessing potential exposure in all markets served by the bank. Members of the team needed access to a wide range of new datasets to develop and test robust models that could then be presented to the regulator for approval.

Pain Points

To develop appropriate models for the IRB approach, members of the credit risk modeling team needed to expand their existing datasets with:

  • Extensive current and historical credit ratings.
  • Pre- and post-credit adjusted financial statement data.
  • The underlying business, financial, industry and economic risk factors and assessments, plus the stand-alone credit profile (SACP) of entities rated by S&P Global Ratings.[2]
  • A flexible data delivery option to easily integrate this information with internal applications.

Thanks to the close partnership S&P Global Market Intelligence (“Market Intelligence”) had with the bank, specialists from the firm started discussions regarding available datasets that could feed the team's models.

The Solution

Market Intelligence specialists described the RatingsXpress solution that provides access to S&P Global Ratings data for issuers and issues. They describe how RatingsXpress could help the team develop backtesting models, conduct risk management benchmarking, implement capital adequacy scenarios on user-defined criteria and assess counterparty risk. With this solution, the team would be able to:

Access current and historical credit ratings

RatingsXpress provides access to current and historical credit ratings and data from S&P Global Ratings, offering:

  • Coverage of 1 million+ credit ratings outstanding.[3]
  • Global, national and regional scale credit ratings on the issuer and issue level.

Improve the comparability of companies

S&P CreditStats Direct™ provides access to analyst-adjusted financial statements for 960+ global banks, 2,700+ corporates and 20,000 revenue sources for U.S. public finance[4] to help improve the clarity, consistency and comparability of credit risk analysis.

Strengthen credit risk modeling

S&P Global Ratings’ Scores and Factors provides access to the underlying business, financial, industry and economic risk factors and assessments, plus the SACP for corporations, banks, insurance companies and sovereigns rated by S&P Global Ratings, enabling users to:

  • Create meaningful financial benchmarks for internal risk models to better understand a company’s financial risk relative to its rated peer group.
  • Understand an issuer’s SACP to uncover hidden risks as a result of weakening support from the parent, affiliate or related government.
  • Rank companies with similar ratings from S&P Global Ratings by assessing a company’s business risk profile, industry risk and competitive position.

Easily integrate data with internal applications

Xpressfeed’s turnkey data loading and maintenance technology provides a reliable and efficient solution for data delivery and management. Xpressfeed’s loader application has automatic schema generation that simplifies maintenance and enables a fully populated database to be up and running quickly. Additionally, the loader runs on the client side, providing greater control over when and how data is processed.

Key Benefits

Members of the credit risk modeling team were interested in enriching their databases with access to a direct ratings feed straight from the source and subscribed to the solution. They are now benefiting from being able to:

  • Develop appropriate models using the IRB approach to calculate capital requirement for regulator’s approval.
  • Potentially reduce the capital requirement with these new models, thereby increasing the amount available to lend.
  • Do rigorous backtesting with extensive historical data.
  • Create risk management benchmarks and ensure analytical comparability among counterparties.
  • Implement capital adequacy scenarios based on user-defined criteria.

The team also expressed an interest in learning more about:

Click here to explore some of the datasets mentioned in this Case Study.

If you are interested in speaking with one of our credit specialists, please click here and someone will reach out and assist you.

 


[2] S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence PD credit model scores from the credit ratings issued by S&P Global Ratings.

[3] Coverage numbers as of January 2022.

[4] The source(s) of funds that are pledged by an obligor to pay the principle and interest on one or more debt instruments.

Learn more about Market Intelligence
Click Here