Consolidating Data for Wholesale Credit Risk Management.
Overview.
A leading UK bank with a vast portfolio of over 1000 credit risk models faced significant challenges in model development, monitoring, validation, and governance. Inconsistent and incomplete input data led to inaccurate risk calculations, increasing the bank's overall risk exposure. The bank sought a comprehensive solution to streamline their data management processes and enhance risk assessment accuracy.
Bank needed help to resolve ongoing issues with their model development, model monitoring, model validation and model governance processes. There were ongoing issues related to inconsistent and incomplete input data that resulted in the results calculated being inaccurate, thereby increasing risk.
Solution.
Analyzed and rectified existing data sourcing and processing issues.
Reviewed and suggested enhancements to the data sourcing process.
Reviewed and suggested enhancements to credit risk models (IRB, IFRS9, Pillar 2 Stress Testing) for PD, EAD, and LGD measurement.
Ensured adherence to Model Risk Policy and Standards as part of the ERM Model Risk Management team.
Provided ongoing support to model development and monitoring activities.
The Impact.
Proposed data preparation solution that addressed sourcing, modelling, and related processing needs.
Improved 1st & 2nd line of defence from Risk Management perspective.
-2.png?length=1920&name=HB%20Item%201%20(3)-2.png)
