The challenge of how much capital is necessary to protect an
organisation against exposure to operational risk losses underpins this pa-
per (operational risk itself is de¯ned as the risk of loss arising from inad-
equate or failed internal processes, people and systems or from external
events). The evolutionary nature of operational risk modelling to establish
capital charges is recognised emphasizing the importance of capturing tail
behaviour. Challenges surrounding the quanti¯cation of operational risk
particularly those associated with sparse data are addressed with mod-
ern statistical methodology including nonparametric smoothing techniques
with a particular view to comparison with extreme value theory (EVT). The
credibility approach employed supports analysis from pooled data across
business lines on a dataset from an internationally active insurance com-
pany. The approach has the potential to be applied more generally, for
example where data might be pooled acro

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operational risk　capital　kernel smoothing　EVT　loss distribution　credibility　pooled data　
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VaR Uses——Operational Risk