Bayesian Network
Definition
Credit Scorecards based on Bayesian Network Models (Also, Tree Augmented Naive Bayes Classifier)
This entry serves as the Abstract Risk Model specification of a Bayesian Network Scorecard
Model Context
A population of borrowers characterised by individual features (characteristics, attributes) associated with each obligor and assumed to represent credit score factors, that is, indicators of propensity to default. The population is modelled statistically for the likelihood for defaulting (or not) over a defined period of time (the Risk Horizon).
Model Classification
The model belongs to the following categories
- generative (the population characteristics are connected in a graph)
- parametric (a set of weights represents the parameters of the model)
- exclusively observed variables
- (generalized) linear
- supervised (the historical default behaviour represents the label)
- elementary algorithm
- frequentist approach
Model Description
Response Variable
The response variable follows a Bernulli distribution. The response variable captures the realization or not of a Credit Event involving the i-th borrower:
Explanatory Variables
The Explanatory Variables form an directed acyclical graph (DAG)
Model Parameters
- Network Structure
- Parameter Weights
Model Estimation
Markov Chain Monte Carlo
Stylized Model Assumptions
See Also
{{#set: isDefinedBy | https://www.openriskmanual.org/ns/doam# }}
__SHOWFACTBOX__