**Abstract**

Financial institutions are concerned about various forms of risk that might impact them. The management of these institutions has to demonstrate to shareholders and regulators that they manage these risks in a pro-active way. Often the main risks are caused by excessive claims on insurance policies or losses that occur due to defaults on loan payments or by operations failing. In an attempt to quantify these risks, the estimation of extreme quantiles of loss distributions is of interest. Since financial companies have limited historical data available in order to estimate these extreme quantiles, they often use scenario assessments by experts to augment the historical data by providing a forward-looking view. In this chapter, we will provide an exposition of statistical methods that may be used to combine historical data and scenario assessments in order to estimate extreme quantiles. In particular, we will illustrate their use by means of practical examples. This method has been implemented by major international banks and based on what we have learnt in the process, we include some practical suggestions for implementing the recommended method.

**Keywords:** operational risk, loss distribution approach, aggregate loss distribution, historical data, measures of agreement, scenario assessments
