Credit risk

Automation of credit risk monitoring using statistical models

This article is written and published by S&P Global Market Intelligence, an independent division of S&P Global Ratings. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit ratings from credit ratings issued by S&P Global Ratings.


S&P Global Ratings’ Issuer Credit Ratings (ICRs) assess a company’s willingness or ability to repay its debt on time and in full. They are based on criteria that include both[1] and qualitative considerations,[2] and are subject to committee review. S&P Global Ratings provides additional directional information (e.g., credit outlook and CreditWatch) on potential future change in certain KPIs,[3] but any credit rating can be updated at any time, with critical implications for asset managers and credit risk managers:

  • Asset managers: A fund’s investment universe is often limited by a minimum credit rating threshold. This tells investors about the risk tolerance a fund is willing to take. When building or rebalancing a portfolio, it is therefore important for asset managers to monitor the overall credit risk profile of their portfolio so that they can stay in line with investors’ expectations or capture potential arbitrage opportunities.
  • Credit risk managers: transitions to or from investment grade can have a significant impact on the risk profile of companies included in a credit risk portfolio,[4] as well as in the calculation of expected credit loss according to the International Financial Reporting Standard (IFRS) and the Current Expected Credit Loss (CECL) standard.

In this article, we show how a statistical model developed by S&P Global Market Intelligence can be used to generate early warning signals of a potential deterioration/improvement in credit risk that could trigger portfolio rebalancing.

CreditModel™ Enterprises 3.0

At S&P Global Market Intelligence, we have developed a quantitative model trained on the Autonomous Credit Profiles (SACPs) of S&P Global Ratings companies:[5] CreditModel™ Enterprises 3.0 (CM3.0). CM3.0 generates a credit score[6] which aims to statistically match the SACP for rated companies, and can also be used for unrated companies above a certain turnover threshold. CM3.0 credit ratings are expressed on the same scale as S&P Global Ratings, but in lowercase nomenclature to distinguish them from actual S&P Global Ratings credit ratings.

Another overlay adjusts the CM3.0 score by including parental and governmental support considerations, if any, allowing the model results to statistically match the S&P Global Ratings KPIs in 28% of cases, as shown in Table 1 for rated non-financial companies. domiciled in North America.[7] As expected for a statistical model, the agreement of the ratings is not perfect (ie for 100% of the cases), because the KPIs include qualitative aspects which cannot be exactly quantified. Anyway, the CM3.0 retains good performance at less than one, two and three notches.

Table 1: Performance of CM3.0 North America, adjusted with parental and governmental support.

Source: S&P Global Market Intelligence as of December 31, 2018. For illustrative purposes only.

Interestingly, we notice a fraction of outliers whose adjusted CM3.0 score is three or more notches different from the true ICR (around 12%). This group seems to defeat any technical attempt to improve the performance of the model. Two questions naturally arise:

  1. What if there was something else behind the existence of these outliers, outside of the inherent limitations of a quantitative model?
  2. What if these outliers could be used to play in a user’s favor by generating early warning signals of a potential change in credit risk?

Our discoveries

Figure 1 illustrates the historical frequency of ICR changes for companies where the adjusted CM3.0 score deviates by x notches from the S&P Global Ratings ICR at time t. For example, the bar corresponding to +1 designates companies whose CM3.0 score is one notch higher than the ICR score at time t, and which have been upgraded (green), downgraded (red) or remained unchanged in within one year (left panel) or within a time horizon of five years (right panel) from time t. The dataset includes companies domiciled in the United States and Canada, rated between 2010 and 2021.

Figure 1: Percentage of rating downgrades, upgrades, no change.

Source: S&P Global Market Intelligence as of October 1, 2021. For illustrative purposes only

As the notch difference widens, the historical probabilities of an ICR change within the specified time horizon increase. As shown in Figure 1, outliers (companies with a difference of three+ notches) are particularly suitable for monitoring purposes, since the statistical probabilities of an upgrade (+three+ notches difference) or downgrading (notch difference of -three and less) are close to 80%.

Table 2 shows the percentage of ratings with an Outlook/CreditWatch before they changed, for a positive or negative one notch difference in Figure 1.

Table 2: Outlook/CreditWatch for ratings that came close to CM3.0 score.

Source: S&P Global Market Intelligence as of October 1, 2021. For illustrative purposes only.

About half of all ratings that were upgraded (downgraded) within a year of registering a positive (negative) notch difference initially had an S&P Global Ratings Outlook/CreditWatch.[8] Equally interesting, about 2% (7%) of ratings with a positive (negative) initial opinion were actually downgraded (upgraded).

Thus, statistical outliers could be used to automate the generation of directional signals of potential improvement or deterioration in creditworthiness, beyond the existing CreditWatch and Outlook.

These results also hold for a five-year time horizon, despite the volatility associated with a longer time horizon and the smaller number of observations that smooth the effect.


The stability or change of a credit rating is important for risk management and investment purposes. In this article, we show how a quantitative model designed to statistically match S&P Global Ratings ICRs can be used to automate risk monitoring or identify potential investment opportunities by appropriately exploiting the natural existence of mathematical outliers. generated by the model.

Our empirical analysis on historical data suggests that a significant divergence between a modeled score and the actual ICR is accompanied by a higher probability of a rating change, beyond the Outlook or CreditWatch indicator sometimes provided. by S&P Global Ratings. As is evident, these signals are statistical in nature and cannot predict rating moves deterministically.

In a follow-up analysis, we will explore how/if the same approach can be leveraged for stock portfolio selection and the search for excess returns relative to a certain benchmark, since rating moves are notorious for their impact. on stock prices.

About S&P Global Market Intelligence

At S&P Global Market Intelligence, we understand the importance of accurate, in-depth and insightful information. We integrate financial and industry data, research and news into tools that track performance, generate alpha, identify investment ideas, perform valuations and assess risk credit. Investment professionals, government agencies, corporations and universities around the world use this essential information to make business and financial decisions with conviction.

S&P Global Market Intelligence is a division of S&P Global (NYSE: SPGI), the world’s leading provider of credit ratings, benchmarks and analytics in global capital and commodity markets, offering ESG, in-depth data and insights into critical business drivers. S&P Global has more than 160 years of providing essential information that unlocks opportunity, drives growth and accelerates progress. For more information, visit

[1] For example, a company’s financial ratios, macroeconomic scenario projections, etc.

[2] For example, country risk, industry risk, competitiveness, peer benchmarking, quality of management, etc.

[3] Typically, there is a one in three chance of a rating downgrade/upgrade in the next six to 24 months, for companies with a negative/positive outlook. There is a one in two chance of a downgrade/upgrade for companies with a negative/positive CreditWatch in the next three months. See, for example, “Guide to Credit Rating Essentials”, S&P Global Ratings (2019), Available here.

[4] Investment grade includes any rating better than BB+.

[5] A corporate SACP refers to an issuer’s credit rating before any parental or governmental support considerations.

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

[7] Assessed on a training sample containing 2,801 unique companies, scored between 2003 and 2017.

[8] Initially, i.e. the date when the difference between the statistical score and the ICR was recorded.