QUANTIFYING OPERATIONAL RISK IN GENERAL INSURANCE COMPANIES Developed by a Giro Working Party By M. H. Tripp, H. L. Bradley, R. Devitt, G. C. Orros, G. L. Overton, L. M. Pryor and R. A. Shaw [Presented to the Institute of Actuaries, 22 March 2004]
The paper overviews the application of existing actuarial techniques to operational risk. It considers how, working in conjunction with other experts, actuaries can develop a new framework to monitor/review, establish context, identify, understand and decide what to do in terms of the management and mitigation of operational risk. It suggests categorisations of risk to help analyses and proposes how new risk indicators may be needed, in conjunction with more normal quantification approaches. Using a case study, it explores the application of stress and scenario testing, statistical curve fitting (including the application of extreme value theory), causal (Bayesian) modelling and the extension of dynamic financial analysis to include operational risk. It suggests there is no one correct approach and that the choice of parameters and modelling assumptions is critical. It lists a number of other techniques for future consideration. There is a section about how ‘soft issues’ including dominance risk, the impact of belief systems and culture, the focus of performance management systems and the psychology of organisations affect operational risk. An approach to rating the people aspects of risk in parallel with quantification may help give a better overall assessment of risk and improve the understanding for capital implications. The paper concludes with a brief review of implications for reporting and considers what future work will help develop the actuarial contribution. It is hoped the paper will sow seeds for the development of best practice in dealing with operational risk and increase the interest of actuaries in this emerging new topic.
Operational Risk; Enterprise Risk Management; Risk Management; General Insurance; Quantification of Risk; Financial Services; Capital Assessment; Capital Management; People Process and Systems; Curve Fitting; Risk Management; Regulations and Risk; Capital Regulations; Risk Categorisation; Risk Indicators; the Bank of International Settlements; Extreme Value Theory; Stress Tests; Scenario Analysis; Causal Models; Bayesian Techniques; Soft Issues; Myers-Briggs; Belbin Team Roles; Risk Reporting; Professional Guidance
Michael Howard Tripp, Watson Wyatt LLP, Watson House, London Road, Reigate, Surrey RH2 9PQ, U.K. Tel: +44 (0)1737-241144; Fax: +44 (0)1737-241496; E-mail: Michael.firstname.lastname@example.org
# Institute of Actuaries and Faculty of Actuaries
Quantifying Operational Risk in General Insurance Companies
In physical science the first essential step in the direction of learning any subject is to find principles of numerical reckoning and practicable methods for measuring some quality connected with it. Lord Kelvin (1824-1907) The lament of many an Institute working party member: ‘‘It is late and I want to go home.’’ Shaw (3 December 2003)
1.1 The Starting Point 1.1.1 In the wake of a number of recent business failures and results which the stock markets see as overly volatile (or unpredictable), an even more professional and thorough scientific approach to business and risk management is increasingly sought. 1.1.2 The financial services industry (be it banking, insurance or securities) has its own approaches to risk that recognise the specific nature of the business environment. Led by the thinking of working groups at the Bank of International Settlements (Basel), typical risk categories include (a) credit risk; (b) market risk; (c) liquidity risk; (d) insurance risk; (e) operational risk; (f) other risk (strategic and reputational risk); and (g) group risk. 1.1.3 The immediate trigger for this paper is a call from the Financial Services Authority (FSA) for...
The risk classification shown in this matrix is based on that in Annex 2 of the working paper on the treatment of operational risk (BCBS, 2001). We show the perceived risk to the bottom line of MELG for three business units.
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