Operational risk quantification and modelling within Romanian insurance industry

Author:

Tudor Răzvan1,Badea Dumitru1

Affiliation:

1. The Bucharest University of Economic Studies , Bucharest , Romania

Abstract

Abstract This paper aims at covering and describing the shortcomings of various models used to quantify and model the operational risk within insurance industry with a particular focus on Romanian specific regulation: Norm 6/2015 concerning the operational risk issued by IT systems. While most of the local insurers are focusing on implementing the standard model to compute the Operational Risk solvency capital required, the local regulator has issued a local norm that requires to identify and assess the IT based operational risks from an ISO 27001 perspective. The challenges raised by the correlations assumed in the Standard model are substantially increased by this new regulation that requires only the identification and quantification of the IT operational risks. The solvency capital requirement stipulated by the implementation of Solvency II doesn’t recommend a model or formula on how to integrate the newly identified risks in the Operational Risk capital requirements. In this context we are going to assess the academic and practitioner’s understanding in what concerns: The Frequency-Severity approach, Bayesian estimation techniques, Scenario Analysis and Risk Accounting based on risk units, and how they could support the modelling of operational risk that are IT based. Developing an internal model only for the operational risk capital requirement proved to be, so far, costly and not necessarily beneficial for the local insurers. As the IT component will play a key role in the future of the insurance industry, the result of this analysis will provide a specific approach in operational risk modelling that can be implemented in the context of Solvency II, in a particular situation when (internal or external) operational risk databases are scarce or not available.

Publisher

Walter de Gruyter GmbH

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference24 articles.

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2. Badea D., Tudor R., (2016), Operational risk assessment with Bayesian beliefs networks –successful application in other industries. New approaches that could fit Solvency 2, Retrieved February 09, 2017, from http://www.ectap.ro/supliment/international-finance-and-banking-conference-fi-ba-2016-xivth-edition/26/.

3. Branford, K., Naikar, N., Hopkins, A. (2009). Guidelines for AcciMap analysis. In: Hopkins, A. (Ed.), Learning from High Reliability Organisations. CCH Australia, Sydney, Australia, pp. 193–212.

4. Cifuentes, A., & Charlin, V. (2016), Operational risk and the Solvency II capital aggregation formula: implications of the hidden correlation assumptions. The Journal of Operational Risk. doi:10.21314/jop.2016.181.

5. EBA, EIOPA, and ESMA, JC 2015 080 (4 December 2015) Joint Committee Discussion, Discussion paper: Paper on automation in financial advice, Retrieved February 09, 2017, https://www.eba.europa.eu/documents/10180/1299866/JC+2015+080+Discussion+Paper+on+automation+in+financial+advice.pdf.

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