FRTB (SA) product of the year: Bloomberg

As banks navigate the most significant overhaul of market risk capital requirements in more than a decade, the need for consistent, high-quality data and reliable analytics has become a strategic priority. The Fundamental Review of the Trading Book (FRTB) has reshaped how institutions classify instruments, compute risk sensitivities, perform bucketing, apply jurisdiction-specific rules and aggregate capital. In this environment, firms require technology that provides confidence, precision and alignment across front-office, risk, regulatory and compliance functions.

This year’s winner of the FRTB (SA) product of the year, Bloomberg, impressed judges with a solution that addresses the full spectrum of FRTB standardised approach (SA) requirements. Bloomberg’s combination of deeply curated datasets, a robust analytics engine and global jurisdictional coverage helps institutions reduce operational fragmentation and achieve a consistent interpretation of regulatory rules across their business.

While some vendors focus on isolated components of the FRTB framework, Bloomberg’s solution brings together data, analytics, risk factor mapping, bucketing logic, capital calculations and regulatory reporting templates into a coherent, well-governed architecture.

The company’s offering integrates seamlessly with the Bloomberg Terminal and its wider enterprise tooling, but is equally effective as an independent component within banks’ internal systems. This balance of flexibility and consistency is becoming increasingly important as institutions adopt hybrid risk architectures requiring shared data foundations and interoperable components.

Comprehensive, robust and global data for FRTB SA compliance

Bloomberg’s FRTB Data Solution is central to its value proposition. FRTB demands a broad, granular and jurisdiction-specific set of inputs, making data quality and transparency critical. Bloomberg supplies banks with data for standardised bucketing and to support default risk charge (DRC) calculation, reference datasets, fund and index data to assist look-through and non-look-through approaches, historical data for stress-testing and extensive pricing data across exchange-traded and over-the-counter instruments. All of these components are delivered as a consistent enterprise feed via Bloomberg Data License, ensuring that multiple functions across the bank rely on the same data. Data License content is easily accessible through secure file transfer protocol or representational state transfer application programming interface, and is also available natively in all major cloud providers.

In practice, this means that the valuations used in front-office pricing tools can be directly aligned with the sensitivities and risk classifications used in capital calculations and regulatory reporting. This alignment reduces the disputes, reconciliation challenges and operational inefficiencies that traditionally arise when different business units work with different data sources.

The solution’s regional coverage is another significant differentiator. Bloomberg supports Basel Committee on Banking Supervision and major local transpositions, including rules for the US, Canada, the UK, Europe, Japan, China, Hong Kong, Taiwan and Saudi Arabia, with Singapore and additional jurisdictions to come. With the staggered implementation of FRTB worldwide, institutions require a configurable framework capable of running the same portfolio through multiple rule sets. Bloomberg’s architecture enables banks to achieve this without the delays and manual work typically associated with adapting capital systems to new regulatory templates.

Powerful and configurable analytics through MARS

While Bloomberg’s data solution provides the foundation, its analytics engine – the Multi-Asset Risk System (MARS) Market Risk – delivers the computational depth required for FRTB SA. MARS Market Risk is part of Bloomberg’s MARS suite of risk solutions. The module calculates SA sensitivities, jump-to-default metrics, residual risk add-ons and capital components for all risk classes. Clients benefit from a fully mapped risk-factor model aligned with FRTB rules, and can choose either to rely on Bloomberg’s curves, surfaces and pricing analytics or to load their own.

The system integrates directly with Bloomberg’s Order Management System, enabling front-to-middle consistency for sensitivities and risk factors. It also supports pre-trade capital analysis, allowing traders and risk managers to assess the capital impact of a prospective trade before execution. This capability is particularly valuable as firms look to optimise portfolios under more binding capital constraints.

Bloomberg’s approach to fund and index look-through is another critical strength. Under FRTB SA, failure to apply look-through can substantially increase capital charges. Bloomberg incorporates constituent data across a broad universe of exchange-traded funds and indexes, and provides mechanisms for private funds to share holdings securely. This enables automated look-through for sensitivities and default risk capital, a requirement that has become especially important in jurisdictions such as Japan.

MARS is built on an open risk architecture, giving clients the flexibility to map factors, upload market data or integrate their own simulation engines. This design aligns with how many banks now operate, blending internal models with vendor-supplied analytics in modular, interoperable frameworks

Regulatory evolution and client focus

Bloomberg’s updates over the past year reflect regulatory developments and customer demand for more flexible, granular control. The firm has expanded SA bucketing coverage to include the latest US rules and has enhanced DRC risk classifications and loss given default data. 

To support institutions with bespoke data methodologies, Bloomberg is introducing the ability for clients to override specific data fields, including risk buckets, external ratings and issuer classifications. This strikes a balance between regulatory consistency and institution-specific modelling.

The company has also widened its analytics import capabilities, allowing clients to bring externally generated sensitivities into the Bloomberg environment. This creates a unified workflow in which Bloomberg-generated analytics and internal calculations come together as a single capital aggregation framework.

Performance during volatility

While 2025 saw significant market volatility, regulators did not alter the SA framework during that period. Bloomberg focused on reinforcing data quality and helping clients understand how their exposures mapped into buckets under different rule sets. With rising capital sensitivity in stressed markets, the reliability of Bloomberg’s datasets and classification logic became especially valuable.

Institutions depend on predictable, stable FRTB systems during periods of heightened activity. Bloomberg demonstrated that its platform remained aligned with regulatory expectations and operationally resilient when clients needed clarity most.

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