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Posterior modeling of operational losses

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Category: Economy and management

Last Updated on 23 September 2014

Published on 23 September 2014

Hits: 5108

Authors:

V.S. Kanev, Dr. Sci. (Tech.), Associate Professor, Siberian State University of Telecommunications and Information Sciences, Head of Department, Novosibirsk, RF

Yu.V. Shevtsova, Cand. Sci. (Tech.), Associate Professor, Siberian State University of Telecommunications and Information Sciences, Senior Lecturer, Novosibirsk, RF

Abstract:

Purpose. To develop methodological approach for adaptive modeling of operational losses.

Methodology. Methods of artificial intelligence systems theory, probability theory, graph theory, probability logic, theory of decision, mathematical statistics, expert evaluation, etc. were used.

Findings. Methods of identification, evaluation, treatment and monitoring of operational risk have been generalized and systematized. The methodology for decision support system of operational risk management based on Bayesian techniques has been developed. The proposed method of Bayesian modeling of operational risk events has been tested on business processes of macro-regional telecom operators, “Siberia”, “Rostelecom”. Risk factors “data loss during the transfer to the new software or new versions of the software.”

Originality. Analytical capabilities of applying Bayesian techniques in operational risk management has been identified and formalized.

Practical value. We have developed methods for decision support system into operational risk management which can be used by companies in the total system of management.

References:

1. Sazikin, B.V. (2008), Upravlenie operatsyonnym riskom v kommercheskom banke [Operational risk management in commercial bank], Vershina, Moscow, Russia.

Сазыкин Б.В. Управление операционным риском в коммерческом банке / Сазыкин Б.В. – М.: Вершина, 2008. – 272 с.

2. Cruz, M.G. (2002), Modeling, measuring and hedging operational risk,John Wiley & Sons, New York.

3. Pearl, J. (2009), Causality: models, reasoning and inference, Cambridge University Press, Cambridge.

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2014-09-17  341.83 KB  1174

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