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Assessing the Unassessed Whilst Maintaining Comparability

When approximately 90% of global banks are unrated[1], understanding potential credit risks can be seriously challenging. Quite often this “known unknown” runs very deep throughout a business, from the treasury through to the credit risk department. Given mandates vary by department the crucial question is, How does a bank counterpart assess unrated banks given different roles, responsibilities and resources? Furthermore, how do you ensure consistency when making comparisons between rated and unrated banks?

Typical needs for credit analysis like this are driven by:

  • Regulation
  • Relatively high single-name bank exposures
  • Internal desire or external pressures for sophisticated risk management

In the above cases a deep dive analysis is often warranted and thus the analysis must be thorough, reflecting both qualitative and quantitative factors. The optimal solution often takes the form of a credit assessment scorecard, reflecting both financial and business related information (e.g. diversification and management); you might also find that the financial information is often linked to a database to reduce workload.

During our recent webinar “When Banks Default, How Much Can Be Recovered? One Year On”, we utilised financial data from both the SNL Platform[1] and the S&P Capital IQ platform, in conjunction with S&P Global Market Intelligence’s credit assessment scorecard to assign credit scores to unrated banks. This scorecard is based on Standard & Poor’s Ratings Services bank ratings criteria and combines both quantitative and qualitative credit risk factors, associated benchmarks and weightings to produce a numerical credit score[2] which demonstrates an extremely high correlation with Standard and Poor’s Ratings Services bank ratings.

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 The credit score can also be mapped to an internal credit scale and a numeric probability of default, where the latter is based on either internal default data or default data from Standard and Poor’s Ratings Services. This model allowed us to consistently analyse a much larger sample and compare both rated and unrated banks, as would be the need of many bank counterparts.

On the flipside, when the need for efficiency and resource constraints are the primary drivers, a quantitative model may be a better fit. In this study we used the S&P Global Market Intelligence CreditModel Financial Institutions (CreditModel FI) to further increase our sample size. CreditModel FI, a web-based quantitative model, is driven primarily by financial ratios/items, macroeconomic and industry-specific factors. As the model is calibrated using Standard & Poor’s Ratings Services bank credit ratings, the output[3] demonstrates a very high correlation with these ratings.

Whichever approach best fits a particular institution’s needs, the model utilised must demonstrate strong power in assigning credit scores which correspond with public credit ratings.

At the very least we would expect a competent ‘aligned’ model to fulfil the following criteria in relation to rated banks:

  • Produce the same credit score as the public rating in 30% of cases
  • Produce credit scores which are within one notch[4] of the public rating in 60% of cases
  • Produce credit scores which are within two notches of the public rating in 90% of cases

S&P Global Market Intelligence’s Credit Assessment Scorecard for Banks and CreditModel Financial Institutions both comfortably exceed these thresholds:

Credit Assessment Scorecard for Banks and CreditModel Financial Institutions

Concluding Thoughts

Credit ratings are typically the first point of call for information on bank default risk. In cases where banks are unrated, there is a real need to utilise credit models to estimate credit scores and provide counterparties with a holistic credit picture for their entire portfolio. Furthermore, it is crucial to use methodologies which allow the credit scoring of banks from across both the rated and unrated bank universe.

These so called ‘aligned methodologies’ also allow market participants to map credit scores from our models to ratings statistics captured in the S&P Global Market Intelligence CreditPro database. This web based application captures ratings migrations, default and recovery rates across geographies, regions, industries and sectors, which is of key importance when internal default history is scarce.

Please watch out for the next in my blog series Your Two Cents’ Worth: Reflecting You View in Credit Scores

If you would like to hear more on this topic you can watch the webinar replay here. Alternatively, you can request more information on the data and analytical tools used for this analysis here.

[1] The SNL Platform provides extensive global bank coverage with over 25,000 active listed, non-listed and subsidiary banks captured within one integrated platform.

[2] Credit scores are a view of default risk generated by S&P Global Market Intelligence models and are not credit ratings. Credit scores are displayed with lowercase letters to distinguish them from the credit ratings issued by Standard & Poor’s Ratings Services, which is analytically and editorially independent from any other analytical group at S&P Global Inc.

[3] Credit scores are a view of default risk generated by S&P Global Market Intelligence models and are not credit ratings. Credit scores are displayed with lowercase letters to distinguish them from the credit ratings issued by Standard & Poor’s Ratings Services, which is analytically and editorially independent from any other analytical group at McGraw Hill Financial.

[4] One notch is the relative difference between any two adjacent ratings/credit score grades (e.g. one notch difference between ‘A’ and ‘A+’).



For the purpose of this article, rated banks are those institutions which have been assigned a credit rating from Standard & Poor’s Ratings Services only. Unrated banks, by definition, are those which have not been assigned a credit rating from S&P Global Ratings [or other credit ratings agencies].

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