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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Common Practices in Modeling Commercial Real Estate LGD


Global Credit Data has the largest database of defaulted Commercial Real Estate Bank Loans (CRE). CRE is defined differently in different regions.  A comparison of modeling approaches identified three general categories. The degree of sophistication reflected the amount of data available to the bank: 1. Expert Judgment Models, 2. Constrained Expert Judgment Models and 3. Data Driven Haircut Models.

Further analysis of the database reveals; year of default, asset class, loan size and legal status show meaningful variations in LGD. Banks use, and plan to use, the database for Stress Testing, Benchmarking, Calibration, Challenger models and Primary modeling.