One of the first and most important phases in any analytical process, and this is certainly no different when developing OpRisk models, is to cast the data into a form amenable to analysis. This is the very first challenge that an analyst or quant faces when determined to model, measure, and even manage OpRisk. At this stage, there is a need to establish how the information available can be modeled to act as an input in the analytical process that would allow proper risk assessment to be used in risk management and mitigation. In risk management, and particularly in OpRisk, this activity is today quite regulated and the entire data process, from collection to maintenance and use, has strict rules, which in a way reduces the variance in the use of the data across the industry.
The OpRisk framework starts by having solid risk taxonomy so risks are properly classified. Firms also need to perform a comprehensive risk mapping across their processes to make sure that no risk is left out of the measurement process. This is a key process to be accomplished and where a number of firms should be paying more attention.
In this chapter, we lay the ground for the basic building blocks of OpRisk management. First we describe how risk taxonomy works, classifying loss events into the major risk categories. Then we describe the four major data elements that should be used to measure and manage OpRisk: internal loss data, external loss data, ...