A Bayesian network structure for operational risk modelling in structured finance operations / A. D. Sanford in Journal of the operational research society (JORS), Vol. 63 N° 4 (Avril 2012)  

Public ISBD [article]  inJournal of the operational research society (JORS) > Vol. 63 N° 4 (Avril 2012) . - pp. 431–444 Titre : A Bayesian network structure for operational risk modelling in structured finance operations Type de document : texte imprimé Auteurs : A. D. Sanford, Auteur ; I. A. Moosa, Auteur Année de publication : 2012 Article en page(s) : pp. 431–444 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Banking Bayesian networks Operational risk Cognitive mapping Artificial intelligence Index. décimale : 001.424 Résumé : This paper is concerned with the design of a Bayesian network structure that is suitable for operational risk modelling. The model's structure is designed specifically from the perspective of a business unit operational risk manager whose role is to measure, record, predict, communicate, analyse and control operational risk within their unit. The problem domain modelled is a functioning structured finance operations unit within a major Australian bank. The network model design incorporates a number of existing human factor frameworks to account for human error and operational risk events within the domain. The design also supports a modular structure, allowing for the inclusion of many operational loss event types, making it adaptable to different operational risk environments. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v63/n4/abs/jors20117a.html [article] A Bayesian network structure for operational risk modelling in structured finance operations [texte imprimé] / A. D. Sanford, Auteur ; I. A. Moosa, Auteur . - 2012 . - pp. 431–444. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 63 N° 4 (Avril 2012) . - pp. 431–444 Mots-clés : Banking Bayesian networks Operational risk Cognitive mapping Artificial intelligence Index. décimale : 001.424 Résumé : This paper is concerned with the design of a Bayesian network structure that is suitable for operational risk modelling. The model's structure is designed specifically from the perspective of a business unit operational risk manager whose role is to measure, record, predict, communicate, analyse and control operational risk within their unit. The problem domain modelled is a functioning structured finance operations unit within a major Australian bank. The network model design incorporates a number of existing human factor frameworks to account for human error and operational risk events within the domain. The design also supports a modular structure, allowing for the inclusion of many operational loss event types, making it adaptable to different operational risk environments. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v63/n4/abs/jors20117a.html

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