Academic journal article Journal of Risk and Insurance

Risk Measurement and Management of Operational Risk in Insurance Companies from an Enterprise Perspective

Academic journal article Journal of Risk and Insurance

Risk Measurement and Management of Operational Risk in Insurance Companies from an Enterprise Perspective

Article excerpt

ABSTRACT

Operational risk can substantially impact an insurer's risk situation and is now increasingly in the focus of insurance companies, especially due to new European risk-based regulatory framework Solvency II. The aim of this article is to model and examine the effects of operational risk on fair premiums and solvency capital requirements under Solvency II. In particular, three different approaches of deriving solvency capital requirements are analyzed: the Solvency II standard model, a partial internal model, and a full internal model. This analysis is not only of relevance for Solvency II, but also regarding an insurer's Own Risk and Solvency Assessment (ORSA) that is not only planned in Solvency II, but also by the NAIC in the United States. The analysis emphasizes that diversification plays a central role and that operational risk measurement and management is highly relevant for insurers and should be integrated in an enterprise risk management framework.

INTRODUCTION

In the context of new risk-based capital requirements for banks and insurers imposed by Basel II/III and Solvency II, respectively, the discussion about operational risk intensified and especially large insurers are now confronted with the need to develop and implement adequate risk measurement and management instruments to deal with operational risk. In Solvency II, operational risk is defined analogously as in Basel II/III as "the risk of loss arising from inadequate or failed internal processes, personnel or systems, or from external events. Operational risk ... shall include legal risks, and exclude risks arising from strategic decisions, as well as reputation risks" (see European Parliament and the Council, 2009, Article 13, No. 33, Article 101, No. 4). (1) Operational risk is also of high relevance for the National Association of Insurance Commissioners (NAIC), where the potential inclusion of a specific charge for operational risk within the U.S. system of risk-based capital for insurers is discussed (see Vaughan, 2009; PwC, 2012). Besides the new regulatory requirements, cases of high operational losses in the recent past also strongly emphasize the importance and considerable risk associated with operational loss events. One of the most mentioned events in this context is the bankruptcy of Barings Bank in 1995, which was followed by a $1.3 billion loss caused by its rogue head derivatives trader in Singapore. (2) The potential impact of operational losses on an insurer's risk situation is also stressed by figures regarding potential insurance fraud by policyholders, which in the German insurance market, for instance, is estimated to about 4 billion [euro] per year (see Hiebl, Roedenbeck, and Kiefer, 2012). In the third party liability insurance only, 25 percent of all claims are suspected to be fraudulent and for an average motor liability insurance company, losses due to fraud are estimated to 32.5 million [euro] per year (see Hiebl, Roedenbeck, and Kiefer, 2012). (3) The magnitude of these operational loss events in the past strongly demonstrates the need for an adequate measurement and management of operational risks, which is also required according to the new framework Solvency II. The aim of this article is to model and quantify the effects of operational risk from an enterprise perspective by focusing on an insurer's pricing and solvency capital requirements under Solvency II. We thereby compare the Solvency II standard formula with a partial and a full internal model.

A large part of the academic literature concerns the modeling of operational risk. Cruz (2002), McNeil, Frey, and Embrechts (2005), Gourier, Farkas, and Abbate (2009), and Shevchenko (2010), for instance, point out the importance of extreme value theory for calculating aggregate losses by using the loss distribution approach. Another part of the literature empirically analyzes operational loss data. Although most of these studies examine empirical data from the banking sector (see, e. …

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