Sensitive to the need for suitable

Sensitive to the need for suitable tools to develop operational risk, in 2001, the Basel
Committee started a series of surveys and statistics regarding operational risks that most
banks encounter. The idea was to develop and correct measurements and calculation
methods. Additionally, the European Commission also started preparing for the Solvency
II Directive, taking into consideration the operational risk for insurance and reinsurance
companies.
As so, and since Basel and Solvency accords set forth many calculation criteria,
our interest in this work, which was developed in parallel with my work on the PhD
thesis at bank Audi Lebanon and at the research laboratory of ISFA, Claude Bernard
University-Lyon, is to elaborate the different quantitative measurement techniques for
operational risk in financial institutions, (particularly in Banks and Insurance companies).
We are going to present the associated mathematical and actuarial concepts as
well as a numerical application regarding the Advanced Measurement Approaches, and
focus on the qualitative part of operational risk, mostly the potential for large, unexpected
losses, either on a per event basis or within a set time period. Hence, pointing
out the importance of both qualitative and quantitative measurements and highlighting
the necessity of an operational risk framework. Furthermore, we direct our study work
to a more specific type of operational risk which is the estimation risk. We explored such
risk to some extent by the use of scenario analysis based on expert opinion in conjunction
with internal data both used to evaluate our exposure to events. In addition, the study
work includes some reflections regarding the measurement of the error induced on the
SCR through the estimation error of the parameters. Hence, revealing the importance of
calling attention to the reflections on assumptions of the calculations.
In practice, this work attempts to present the different modeling tools for assessing
operational risk and more particularly, pointing up the consequences of estimation risk
behind. This draws the attention to the conclusion that it would be more appropriate
and effective in reality to privilege more simple and prudent models than to complicate
things and generate additional errors and instability.