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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PD Report Large Corporates 2022


This Probability of Default (PD) report covers a reference data set of 92,000 large corporate exposures, provided by 27 banks.

At the pool level, the Through-The-Cycle Probability of Default (TTC PD) is stable over time with a value of around 0.2% on investment grades and 3% on speculative grades, overall at 1.94%.  It is consistently above the observed Default Rate due to regulatory buffer requirements. The Default Rate increases align with crises for speculative ratings. For investment ratings, the default rate was particularly impacted by the Covid crisis.

The insights gained from these high-level analyses confirm the benefits of detailed and granular collection of data – critical for banks using data-driven credit risk modelling to understand and quantify PD