Overview of Exposure at Default Models
Exposure at default (EAD) is the loss exposure for a bank when a debtor defaults on a loan.
For example, the loss reserves are usually estimated as the expected loss (EL), given by the following formula:
EL = PD × LGD × EAD
With increased availability of data, there are several different types of EAD models. Risk Management Toolbox™ supports:
Regression models — These are linear regression models where the response is a transformation of the EAD data. For more information on the supported transformations, see
Tobit models — These are censored regression models with explicit limits on the response values. Censoring on the left, right or both sides are supported. For more information, see
Model Development and Validation
Risk Management Toolbox supports the modeling and validation of EAD models through a family of classes supporting:
Model fitting with the
Prediction of EAD with the
A typical modeling workflow for EAD analysis includes:
Data preparation for EAD modeling requires a significant amount of work in practice. Data preparation requires consolidation of account information, pulling data from multiple data sources, accounting for recoveries, direct and indirect costs, determination of discount rates to determine the observed EAD values. There is also work regarding predictor transformations and screening. There is a wide range of tools available to treat missing data (using
fillmissing), handle outliers (using
filloutliers), and perform other data preparation tasks. The output of the data preparation is a training dataset with predictor columns and a response column containing the EAD values.
fitEADModelfunction to fit an EAD model. You must use the previously prepared data and select a model type. Optional inputs allow you to indicate which variables correspond to predictor variables, or which transformation to use for a
Regressionmodel, or the censoring side for a
Tobitmodel. You can specify a model description and also specify a model ID or tag for reporting purposes during model validation.
There are multiple tasks involved in model validation, including
Measure the model discrimination on either training or test data with the
modelDiscriminationfunction. Visualizations are generated using the
modelDiscriminationPlotfunction. Data can be segmented to measure discrimination over different segments.
 Baesens, Bart, Daniel Roesch, and Harald Scheule. Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS. Wiley, 2016.
 Bellini, Tiziano. IFRS 9 and CECL Credit Risk Modelling and Validation: A Practical Guide with Examples Worked in R and SAS. San Diego, CA: Elsevier, 2019.
 Brown, Iain. Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Applications. SAS Institute, 2014.
 Roesch, Daniel and Harald Scheule. Deep Credit Risk. Independently published, 2020.