Summary
A fictitious online business and an insurance fraud example are used to illustrate how a Bayesian network (BN) can be set up using a combination of past data and expert input. In these illustrations it is also demonstrated the application of BNs in the areas of setting of capital for OR and scenario testing for causal analysis-two important components of supervisory regimes of financial institutions. The model has been shown to be easily adaptable to incorporate new input, and techniques for assessing the suitability of the model have also been demonstrated. Bayesian statistical methodology ensures that the model can quickly adapt to new input and incorporate it with prior expert opinion in a mathematically tractable manner.
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Extract
Modeling Operational Risk with Bayesian Networks
Introduction
Bayesian networks (BNs) have recently been explored as a potential tool for various risk management applications. Its main features of combining subjective opinion with observed data and modeling cause-and-effects make it especially well suited for investigating and capturing the workings of financial institutions. Although its usage has thus far been limited to specific areas (e.g., it has been used for credit risk scoring by banks) its application to wider enterprise risks is being increasingly documented, especially in the area of operational risk (OR).Chapter 14 of Alexander (2003) provides a brief introduction to modeling OR using BNs via a banking example. Marshall (2001), Cruz (2002), and Hoffman (2002) give brief overviews of BNs and where they fit into the whole framework of OR modeling. There is also an illustrative albeit high-level discussion on causal modeling using BNs via a banking example in King (1999).The main purpose of this article is to consider two aspects of the application of BNs to OR in greater detail than has so far appeared in the literature. These are the theory and techniques of model updating, and the subject of model assessment. OR takes place in a dynamic setting, with more information becoming available as time progresses. Hence, it is useful to update the models used for OR to take account of this flow of information. This is feasible within the setting of BNs and was briefly mentioned in King (1999); the section on "Updating the Probabilities With New Data" of this article gives more details of how this can be implemented. In any modeling exercise, it is essential to check that the model used provides a reasonable representation of the actual experience. Again, this is best done dynamically in the OR setting, as more information arrives; this is covered in the section on "Model Assessment" of this article.The article is set out as follows. In the section on "Changes to Supervisory Regimes as a Driver for Operational Risk Modeling," we describe recent developments in the supervision of financial institutions and how this has encouraged greater efforts in OR modeling. In the "Current Approaches to Modeling OR" section, we give a brief introduction to modeling approaches that have been used in the context of OR. The approach used in this article is that of BNs, which are introduced in the next section and applied in a general risk management context in the subsequent section. In the sections on "Updating the Probabilities With New Data" and "Mode...See the full content of this document
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