Challenges of operational risk advanced capital models

Brenda Boultwood and Rafael Cavestany

One particular challenge of operational risk capital modelling is in regard to data quality. Data quality represents the foundation of an operational risk capital model as the quality of the model output cannot exceed that of its inputs. Data quality affects all the four data elements of the capital model: ILD, ED, SA and BEICFs.

ILD must be collected with completeness (BUs, size, risk types and other considerations), ensuring its consistency with accounting, and each event should contain specific data fields appropriately populated. Additionally, the collection should follow a particular definition and methodologies, permitting the correct modelling of operational risk loss distributions (an example of these definitions and methodologies can be found in the Operational Risk Reporting Standards of the Operational Riskdata eXchange Association (ORX)). To guarantee adherence to these, it is necessary to implement a workflow with the corresponding approvals where the data quality is validated before being ratified for quantification. Finally, the ILD collection ideally should guarantee a trail and have adequate data certificates (see Chapter 2).

To obtain an SA with the

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