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.

Access the Library 

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 & Rating Platform

What we do

In 2009 Global Credit Data started a study of Observed Default Frequencies, i.e. the number of defaults actually counted by a bank in a year within a segment of its obligors. This exercise complements the analyses on LGDs and EADs, and, like them, is strictly historical. In 2015, the data template and process has been amended in such a way that also migration matrices and multi-year defaults rates (both relevant for stresstesting and IFRS 9 impairment modelling) are calculated and given back to the participating banks.

The database input for this datapooling is much simpler than the LGD-EAD database: we are simply asking for quarterly “portfolio snapshots”, including basic information such as asset class, country/geographic region, industry, rating category and PD. All this enables us to calculate and give back default rates, migration rates and average PDs for a vast amount of different segments  – information which member banks can use instantly to benchmark their internal PD systems.


How can the database be embedded in your regular processes

Since our last collection exercise our database contains more than 1.5 million obligors and 84,000 defaults through the last economic cycle (from 2002 until 2015) from 24 banks, split out by year, rating, region, industry and asset class. This database allows us peer-benchmarking of internal rating models, construction of correlation matrices and long-term default rates.

Some concrete use cases are: 

  • Benchmark your PD masterscale (comparison PDs and default rates per rating class)   
  • Benchmark your system’s discriminatory power
  • Identify macro-economic dependencies in default and migration data: Extract a “systemic factor” from rating migrations or default rates
  • Benchmark your asset correlations and long term default rates
  • Benchmark your stage allocation / SICR buckets (thresholds for “life-time PD” movements) under IFRS 9 
  • Reduce uncertainty add-ons for lack of data