Quantitative modeling of operational risk losses when combining internal and external data

We present an overview of methods to estimate risk arising from operational losses. Our approach is based on the study of the statistical severity distribution of a single loss. We analyze the fundamental issues that arise in practice when modeling operational risk data. We address the statistical problem of estimating an operational risk distribution, both in standard abundant data situations and when our available data is challenged from the inclusion of external data or because of underreporting. Our presentation includes an application to show that failure to account for underreporting may lead to a substantial underestimation of operational risk measures. The use of external data information can easily be incorporated in our modeling approach.