Credal Networks for Operational Risk Measurement and Management

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外文摘要：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。

外文关键词：

credal networksoperational riskimprecise probabilities

作者：

Alessandro Antonucci、Alberto Piatti、Marco Zaffalon

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作者单位：

Istituto 'Dalle Molle' di Studi sull'Intelligenza Artificiale Via Cantonale, Galleria 2, CH-6928 Manno, Switzerland

会议名称：

Knowledge-Based Intelligent Information and Engineering Systems pt.2: KES 2007 - WIRN 2007; Lecture Notes in Artificial Intelligence; 4692

会议地点：

Santiago de Compostela(ES);Santiago de Compostela(ES)

会议母体文献：

International Conference on Knowledge-Based Intelligent Information and; International Conference on Knowledge-Based Intelligent Information and Engineering Systems(KES 2007); Italian Workshop on Neural Networks; 20070912-14; 20070912-14; Santiago de Com

页码：

P.604-611

出版时间：

2007