Oracle Financial Services FRTB Solution

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Oracle's solution creates a foundational system for the future that gives financial institutions the visibility and flexibility required to comply with FRTB standards.

Key Features

•Full suite of capabilities to support both internal models and standardized approaches

•Reassurance of robust pricing delivered by market leading Numerix engines

•An unrivalled data management and governance framework

Key Benefits

•Core calculation capabilities – Do current systems and infrastructures have the capacity and scale to support the increased computational requirements needed to support extensive expected shortfall calculations with multiple liquidity horizons and intra-day monitoring? Cloud increasingly factors into these considerations as incremental scale and computing power can be secured on demand.

•Data availability – FRTB will require firms to take a careful and thorough look at their data architectures. In the new world, it moves from a sideshow to front and center in the development and execution of risk methodology and strategy. A single source of data becomes imperative.

•Data and model governance – Firms will likely have to implement and manage both SA models as well as internal models with a higher degree of scrutiny. Model development and management is a very iterative and continuous process requiring robust what-if modeling capabilities, validation and testing stages, updates, and approval―only to begin the process again. Automation of both data and model management becomes a priority as firms seek to achieve required process standardization and traceability as well as reduce the cost and resource burden of these new requirements.

•Reporting – Under FRTB, firms experience greater need to manage and execute regulatory and risk reporting in a single integrated environment. It must automate end-to-end processes from data capture through submission, and include powerful governance capabilities that enable firms to elevate data integrity to new levels while streamlining and reducing the resource burden of compliance. This approach frees analysts to spend more time on gaining new insight from growing stores of data instead of simply preparing data and reports with the sole objective of meeting deadlines.


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