- About GCD
- Data Pooling: PD & LGD
- Library: Research & Publications
- Utility: Analytics & Products
- Services: Benchmarking
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).
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.
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.
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.
- This event has passed.
Kick-off meeting “LGD Reference Model”
March 14, 2019 @ 12:00 pm - 1:00 pm UTC+1
In 2018, GCD has published for the first time its LGD Report Large Corporate – examining the Loss Given Default (LGD) for banks lending to corporate borrowers with a turnover of more than €50m. The report finds that banks, on average, recover 75% of debts owed by large corporate borrowers after default and confirms the hitherto untested principle that seniority and collateral drive low rates of LGD.
Following this, we now move into a new phase.
GCD has partnered with FCG to help banks to benchmark their LGD models with a reference model to be built on GCD data.
The work will include
- Building a RDS on GCD’s increasing data set
- Exploration of traditional modelling techniques (to identify drivers of LGD)
- Applying machine learning techniques
- Discussion on the requirements of a good challenger model/method to be embedded in banks’ processes
- Analysing unsecured as well as secured facilities, including different kinds of collateral
- Publication
Members have been invited to join the kick-off meeting and share their view on the concrete deliverables and how this working group should be run in order to ensure an outcome which is useful for members and publishable.
The meeting has been recorded and the recording is available here.
Location:
Zoom call