Modeling operational risk with Bayesian networks

Modeling operational risk with Bayesian networks

0.00 Avg rating0 Votes
Article ID: iaor20084369
Country: United Kingdom
Volume: 74
Issue: 4
Start Page Number: 795
End Page Number: 827
Publication Date: Dec 2007
Journal: Journal of Risk and Insurance
Authors: , ,
Keywords: Bayesian modelling
Abstract:

Bayesian networks are an emerging tool for a wide range of risk management applications, one of which is the modeling of operational risk. This comes at a time when changes in the supervision of financial institutions have resulted in increased scrutiny on the risk management of banks and insurance companies, thus giving the industry an impetus to measure and manage operational risk. The more established methods for risk quantification are linear models such as time series models, econometric models, empirical actuarial models, and extreme value theory. Due to data limitations and complex interaction between operational risk variables, various nonlinear methods have been proposed, one of which is the focus of this article: Bayesian networks. Using an idealized example of a fictitious on line business, we construct a Bayesian network that models various risk factors and their combination into an overall loss distribution. Using this model, we show how established Bayesian network methodology can be applied to: (1) form posterior marginal distributions of variables based on evidence, (2) simulate scenarios, (3) update the parameters of the model using data, and (4) quantify in real-time how well the model predictions compare to actual data. A specific example of Bayesian networks application to operational risk in an insurance setting is then suggested.

Reviews

Required fields are marked *. Your email address will not be published.