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 Benchmarking Report 2019


This report, which is designed to help banks benchmark their Probability of Default (PD) estimates against industry peers, highlights the conservative nature of banks’ internal PD estimates, with average PD estimates for the global corporate segment over the last 15 years standing at 1.63% compared to an average default rate of 0.90% over the same period. 

For the report, GCD collected information regarding long-term internal observed default rates and internal rating migration matrices from a portfolio of 26 leading financial institutions, over a period of 15 years. This data is highly valuable for benchmarking key risk processes within banks, such as PD rating scale calibration, PD model calibration, regulatory and economic capital calculation and stress testing, among others.

Banks are required to  compare their internal results to external benchmarks, such as those produced by credit rating agencies (CRAs). However, while CRA benchmarks are typically based on bonds, this study provides a bespoke alternative to CRA reports – comparing the same instruments to provide a more accurate representation. Due to the specific underlying instruments, wider coverage of counterparties and different sensitivities of rating systems to the macro-economic cycle, banks’ internal observed default rates and rating transition matrices behave very differently from those established by CRAs.