LGD Modelling

We have successfully developed and implemented LGD models in several banks around the world. Our selection of the ideal model generally depends on:

  • Data availability for the key indicative factors for predicting the recovery rates
  • Validation of the methods to determine the best fit for the bank’s financing portfolio


Our LGD model development framework incorporates the following build strategies:

  • A hybrid Expert and Data-driven model that relies on the confidence level on the quality and richness of LGD data. This technique is widely referred as Bayesian approach for LGD model
  • Workout Model-Involves discounting of recovery cash flows received after the loan has defaulted in order to compare the net present value of recovery streams with the EAD
  • Additive Linear Model-Designed to estimate an outcome directly. This development requires the availability of qualitatively and quantitatively flawless data
  • Regression Tree-A non-parametric and non-linear predictive model in which the predictive value of the target variable is obtained through a series of sequential logical if-then conditions

EAD Modelling

As with other credit models, we have successfully developed and implemented EAD models through a tried and tested framework.

  • We employ a model development process that is comprehensive to address all elements that contribute to exposure forecast modeling
  • We analyze requirements of the bank, based on which we develop a customized model landscape, determining the number of EAD models to be developed
  • Our EAD Model Framework is aligned with the LGD Model given the close relationship between the risk models