By Peter Irvine, Head of Product, and Gaspard BiosseDuplan, Head of Sales & Trading Product at Acin
Operational Risk, as a discipline in the banking sector, has always struggled with the lack of sufficient, robust and quantifiable data. Data has often seemed to be at the heart of some of its fiercest controversies and challenges.
All this could be about to change with the introduction of a fresh approach to OpRisk data that is anchored in the methodologies of today’s data management revolution. As a result, the value that Operational Risk teams will be able to deliver to their organizations should increase substantially.
Defining “OpRisk data”
First, what is meant by the term “OpRisk data”?
Up until recently, the phrase usually provoked thoughts about loss data – the information that firms collect when things go wrong with people, processes, systems, or external events. Today there are several loss data consortiums that collect this information and redistribute it back to firms.
However, the truth is thatOpRisk data – as a category – is much broader than just loss event data in the same way that credit risk data is much broader than just historical loss events.OpRisk data includes the fluctuating metadata of the risk taxonomies,key controls libraries, and operating performance data from the control environments itself, such as risk indicators. Finally, the data collected and produced from the risk and control self-assessments (RCSAs) should also be considered part of this dataset, as a cornerstone component ofthe risk profile view and decision making…