Credal networks for operational risk measurement and management
Authors: Alessandro Antonucci, Alberto Piatti and Marco Zaffalon
Abstract: According to widely accepted guidelines for self-regulation, the capital requirements of a bank should relate to the level of risk with respect to three different categories. Among them, operational risk is the more difficult to assess, as it requires merging expert judgments and quantitative information about the functional structure of the bank. A number of approaches to the evaluation of operational risk based on Bayesian networks have been recently considered. In this paper, we propose credal networks, which are a generalization of Bayesian networks to imprecise probabilities, as a more appropriate framework for the measurement and management of operational risk. The reason is the higher flexibility provided by credal networks compared to Bayesian networks in the quantification of the probabilities underlying the model: this makes it possible to represent human expertise required for these evaluations in a credible and robust way. We use a real-world application to demonstrate these features and to show how to measure operational risk by means of algorithms for inference over credal nets. This is shown to be possible, also in the case when the observation of some factor is vague.
Details: In Apolloni, B. and Howlett, R. J. and Jain, L. C. (Eds.), Proceedings of the 11th International Conference on Knowledge-Based and Intelligent Information \& Engineering Systems: KES2007, Lectures Notes in Computer Science. Springer, pp. 604-611.
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