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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Homogeneity in the Large Corporates Unsecured LGD


When using historical data for Loss Given Default (LGD) modelling, it is advisable to check the homogeneity of obligors and exposures assigned to the same grade or estimate – said differently: it is important to prove that no further relevant risk differentiation is possible. Large Corporate unsecured exposures, given their global cash flow and assets, are often argued to be a global and homogeneous asset class. For which –due to the relative lack of internal historical data within banks– is often produced a set of base estimates of the unsecured LGD risk parameter. In this document, we provide the results of an analysis performed to investigate whether this asset class can be considered as homogeneous for LGD modelling. We performed univariate analyses by relevant potential risk differentiation drivers: Large (GSIFI) banks vs. other lenders, Exposure size, Lenders’ regions and Borrowers’ regions; for which no significant potential for risk differentiation was found. However, using bivariate analyses crossing Lenders and Borrowers’ regions – i.e. out of region lending, we found potential for further risk differentiation.