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
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
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
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.
function 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
model, or the censoring side for a
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
modelDiscrimination function. Visualizations
are generated using the
modelDiscriminationPlot function. Data can
be segmented to measure discrimination over different
 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.