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Embedded in CRS workflow
Hassle-free comprehensive LGD calculations
The LGD model is embedded in our Credit Risk Suite, guaranteeing hassle-free LGD assessments as part of your ECL calculations.
Comprehensive LGD insights
Assess LGD on individual facility level
The LGD model provides insight in the LGDs on a facility level.
Get in touch with our experts
Leveraging on years of experience
The Zanders LGD model is an expert-based model that leverages on years of experience.
Benchmarked on other LGD models
In line with market practice
Zanders is constantly in touch with relevant industry players and performs benchmarking of its internal approach versus other approaches in the market, to ensure its approach remains in line with market practice.
Our LGD model takes into account a variety of drivers, including seniority, collateral type and coverage, country of risk, collateral claim ranking, additional security and guarantees and cross-collateralization.
LGD model methodology
The process for determining the LGD consists of the following steps:
- Seniority of transaction: A minimum recovery rate is determined based on the seniority of the transaction.
- Collateral coverage: The collateral coverage of the facility is determined in order to estimate the total recovery rate.
- Mapping to LGD class: The total recovery rate is mapped to an LGD class using an LGD scale.
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model fits your portfolio?
Customized models, because no portfolio is the same
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Our credit risk experts can customize our model to fit your portfolio. Find out how we optimize our models for your use case
All support and updates are included
More about your benefits
Functional and technical questions are addressed quickly. Additionally, all our clients benefit from each upgrade to ensure continued compliance and ease of use.
Our quality guarantee
Data-driven calibration to optimize model performance
Annual recalibration of our model to incorporate latest economic trends. Using a database containing millions of companies
We include the insights from the portfolios of our clients, to improve our model performance