| Titre : | A Bayesian network structure for operational risk modelling in structured finance operations (2012) |
| Auteurs : | A. D. Sanford, Auteur ; I. A. Moosa, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Journal of the operational research society (JORS) (Vol. 63 N° 4, Avril 2012) |
| Article en page(s) : | pp. 431–444 |
| Note générale : | Recherche opérationnelle |
| Langues : | Anglais |
| Index. décimale : | 001.424 |
| Tags : | Banking Bayesian networks Operational risk Cognitive mapping Artificial intelligence |
| 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 |
