GCD's Mission is to help banks understand and model credit risks. The comprehensive data pools are collected over a decade and distributed back to members for their own research and modelling.


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GCD is a unique data consortium that owns banks internal data for both PD and LGD. GCD’s data pools support the key parameters of banks’ credit risk modelling: Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD).

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GCD’s library gives access to wide variety of publications on risk related topics. Global Credit Data members work together to analyse the data and discuss methodology issues. GCD has published numerous papers and is actively promoting academic research on the data collected.

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Members not only benefit from exclusive rights and access to credit databases and analytics, but also from knowledge and research facilitation possible via the unique industry association.

Through a variety of forums such as workshops, webinars and surveys, GCD is an active industry participant facilitating the discussion in key strategic areas.

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Global Credit Data collects raw data from its members and distributes it back to them for use in their own analysis and modelling. GCD supports its members by providing a flexible high-end tool on the data pool: the GCD Visual Analyzer. Member banks can create dynamic Reference Data Sets and generate instant views on the data.

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IFRS 9 Benchmarking Study

GCD is running regularly benchmarking studies and hypothetical portfolio excercises, in order to support its members with an independent view on how their credit risk estimates compare to peers. 
Our IFRS 9 benchmarking is now fully integrated in our benchmarking work, more information is available with Hale Tatar
The results from previous excercises are summarized in our IFRS 9 Benchmarking Report 2019 (August 2019)
Summary of the results:
  • Variability of ECL estimates is noticeable for all asset classes
  • The variability between banks’ estimates is observed for all segments defined by ECL drivers such as obligor type, geography, industry, rating and PD, facility type, guarantees and collateralization
  • Bank-specific or reference macro-economic scenarios used for projections led to identical conclusions: in the current macro-economic environment, the variability between banks is mainly caused by banks’ different models and not by different macro-economic forecasts
  • In order to “measure” the variability, we introduce a multiplier (=ECL 3rd quartile / ECL 1st quartile, calculated over all participating banks). We see that the multiplier is fairly stable over all asset classes and – on average – stands at least at a level of 4.
  • Projections of stress test scenarios logically increase ECL levels and also notably increase the variability between banks 
Why should your bank participate in our benchmarking work: 
  • Determine how your bank’s IFRS 9 estimates compare to that of peer banks. 
  • Neutral to your bank’s portfolio or macro-economic forecast
  • Be able to track down the reason for the variability, e.g. 
    • Does the difference lie in the PD estimation, the LGD estimation or the exposure calculation? 
    • How much is the 12-month ECL or the lifetime ECL impacted by banks’ economic forecast? 
    • Is your bank’s stage allocation process more or less conservative than that of other banks? 
    • Do banks PD curves differ by country? How many banks apply LGD term structures? 
    • In which countries is the variability between banks the most? 
    • How does the expected life of revolving facilities differ between banks? 
  • “By banks, for banks”: We value your input in changing the study to your needs
  • Aligned with GCD’s other datapools: Easily explain further differences making use of GCD’s
    • Benchmarking platform: Benchmarking estimated PD, LGD and CCFs on name-by-name basis / by “risk cluster”
    • Ratings and PD platform: Benchmarking observed default rates by “risk cluster”
    • LGD/EAD platform: Benchmarking observed LGDs and CCFs
  • Based on GCD’s datapooling infrastructure: Highly-secured data portal and datapooling regulations ensure maximum confidentiality  
  • Ultimately, strengthen your IFRS 9 model development and validation process