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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Corporate loan recovery rates much higher than assumed, confirms second Global Credit Data report on LGD

by | Mar 29, 2019 | Newswire Page, Uncategorized

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PRESS RELEASE – April 2nd, 2019

For the second year running, Global Credit Data releases extensive analytics on loss given default, confirming the positive results from 2018

Global Credit Data (GCD), a not-for-profit data-collection initiative jointly owned by more than 50 leading global banks, has today released its second authoritative report on loss given default (LGD) for large corporate borrowers with a turnover above €50m. The LGD Report 2019 reveals that long-term recovery rates for large corporates are significantly higher than industry estimates. According to the data, banks recover, on average, 76% of debts owed by large corporate borrowers after default. This is significantly higher than the 55% recovery rate implemented by the Basel Committee on Banking Supervision for corporate exposure under the foundation IRB approach.

The findings are based on a reference data set comprising 10,737 defaulted borrowers and 18,465 facilities, from 58 lenders worldwide.

“With three new lenders, over 1,000 more borrowers with more than 1,500 loans and an additional year’s worth of defaults, these results are based on a larger and richer data set than last year’s industry-standard report,” says Nunzia Rainone, Methodology and Membership Executive, Global Credit Data. “The fact that the findings are in line with the previous report confirms the stability and consistency of our data sets and the reliability of our long-term estimates.”

Results also reveal that seniority and collateral remain important tools for minimising LGD. Indeed, LGD outcome is usually lower for collateralised defaults, supporting common bank lending policies that assume that the taking of collateral will improve their position.

The data indicates that secured LGDs are generally lower than unsecured (22% vs. 27% on obligor level; 22% vs. 25% on obligation level). Meanwhile, for unsecured LGD, seniority is confirmed as a driver at obligor level (26% senior vs. 38% subordinated) and obligation level (24% senior vs. 36% subordinated).

The time banks take to recover defaulted debt is also quicker than might be expected. While time to resolution is two years on average, the average time to recovery (TTRec) – the average period between default and cash flow payment weighted by the amount of the payment – is only 1.2 years. As a more objective measure of the time in default, only dependent on the time to cash flow, TTRec represents a useful tool for understanding discount rates on LGD. Outcomes for TTRec are also remarkably similar across differing collateral and seniority states.

“Insights gathered from the LGD Report 2019 continue to confirm the benefit of detailed and granular collection of post-default cash-flow data,” adds Richard Crecel, Executive Director at Global Credit Data. “The consistency of our data sets – compiled from our members’ own high-quality data – allows for objective analysis of LGD drivers. As a non-profit organisation, we insist on this high quality of data from our members – which drives detailed high-quality outputs and well-informed decisions.”

GCD also provides its members with the detailed data sets and extensive peer comparison reports, allowing them to test and confirm results with customised sub-sets of data. This gives banks the opportunity to benchmark their own results against those of their peers and carry out detailed analyses of often complex considerations such as the impact of collateral.

Click here to download the full report.

Click here to download the Appendix: Database & Methodology


About Global Credit Data (GCD)

Global Credit Data (GCD) is a non-profit association owned by its 55 member banks from around the world. Its simple mission is to help banks better understand and model their credit risks through data pooling and benchmarking activities.

GCD’s LGD & EAD Platform is the world’s largest database for LGD and EAD modelling totalling over EUR 200 billion in all Basel Asset Classes. In 2009, GCD introduced a PD & Ratings Platform which now covers more than 15 years of data and helps banks to calibrate and benchmark their PD models in use for regulatory & economic capital, stress-testing, impairment calculation and pricing.

In 2011, GCD started a third database: The Benchmarking Platform. The database includes borrower name and cluster level estimates to help banks to instantly compare PD, rating, LGD and EAD model estimates with their peers. The robustness and capacity of GCD’s data collection and management infrastructure places GCD databases as the global standard for credit risk data pooling.




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