Much regulatory guidance reinforces the need for banks to have a robust set of model risk management processes in place. FRTB, PRA Model Risk Management, TRIM, and Fed Model Risk Management are key among them.
To achieve regulatory standards requires the evolution of model ecosystems in the front office, finance and risk. A key element of this evolution for many banks will be data lineage, involving instrument lineage, market data traceability, model validation lineage and robust product taxonomies. And black-box approaches to the production of fair values and capital charges are no longer acceptable. A data centric approach to model risk management is essential.
Deriving tangible business benefit from this necessary evolution will only be possible by bringing analytics and data closer together, ensuring that quants teams are always accessing validated data, and are able to do so faster and easier, from a common source.