FRTB: The Data Management Challenges & Opportunities for Banks

Dihan Rosenburg

The Fundamental Review of the Trading Book (FRTB) goes live Jan. 1, 2023. It’s one of the most significant and complex updates to market risk regulation in decades. Banks that get it right may save millions of dollars from...

The Fundamental Review of the Trading Book (FRTB) goes live Jan. 1, 2023. It’s one of the most significant and complex updates to market risk regulation in decades. Banks that get it right may save millions of dollars from being tied up in capital reserve requirements. While the data management challenges of this new law are far-reaching, at its heart is a question that ASG helps banks answer every day: “Where is my data coming from?”

What is the FRTB?

The Fundamental Review of Trading Books, “FRTB” is the last piece of the Basel III regulatory package, a sweeping overhaul introduced in the wake of the 2007 global financial crisis. The changes forced banks around the world to raise their capital reserves and data management practices to make them better prepared to withstand market downturns without government bailouts.

FRTB aims to create better boundaries between a bank’s trading and banking desks to better account for the additional risks that trading desks incur. Trading desks need higher capital reserves because they trade in short-term investments like stocks and bonds that are more volatile than banking books, which have more stable investments that are generally held until maturity.

FRTB default risk manuals vs. internal models

Banks may accept the Basil Committee’s (BCBS) reserve calculations or develop their own internal risk models to calculate capital reserve requirements. FRTB default reserve requirements are higher than banks’ internal models; thus, banks prefer to create internal risk models to lower reserve requirements and free up cash flow for investment, revenue and profits.

To use their internal models, banks must obtain the approval of national regulators by proving how well models represent risk in the banks’ investment strategies. The approval process requires a bank to forecast simulated profits and losses using its model’s calculated capital reserves, as well as to back test the model with real pricing and holdings data dating back to 2007. Banks must also model expected shortfall calculations to address worst-case scenarios in their investment strategies.

Data challenges to consider

Banks will need to aggregate data from many disparate sources to build FRTB reports and calculate capital charges. Some of the data management challenges include the following:

  • Distinct boundary between banking and trading desks - Transactional data will need to be strictly classified into one of these two categories
  • New reporting requirements – Changes include accounting for intraday risk and the comparison between risk management and pricing models, as well as additional reference data management obligations
  • Need to show a strong relationship between the data and calculations in the risk and finance departments - Historically, these have been separate functions since risk is more forward-looking, while finance reports are historical.  
  • Models must be maintained – Once a bank has secured regulatory approval for their internal model, it must be maintained and kept current.
  • New risk sensitivities will need to be calculated – Even banks using internal models will need to calculate their market risk and capital charge using the standardized method. These sensitivities must be computed in the front-office systems and then integrated with the FRTB aggregation element, which can be a challenge for large banks with multiple front-office systems.

How data lineage helps

Data lineage example

Originally a tool used by IT professionals for impact analysis and data lifecycle management, data lineage gained wider adaption in the banking industry after the 2007 global financial crisis. Banks were unable to report accurate risk exposure because they lacked the ability to aggregate risk exposures and identify concentrations quickly and accurately at the bank’s group level.

The Banking Supervision regulation #239 (BCBS 239) introduced 14 principles for effective risk data aggregation and risk reporting. At its essence, banks must be able to demonstrate the data flow used to create risk reports, hence the need for data lineage.

The complex demands of FRTB make having current, sustainable data lineage even more crucial for compliance.  With it, banks can:

  • Trace the lineage of risk factors back to their original, authoritative data sources
  • Span multiple data silos into a unified dataset
  • Create a visual representation of the data flow (data lineage)
  • Work with regulators to visualize and modify risk models
  • Enable the easy modification of risk models to address changing market conditions, organizational changes and investment strategies
  • Establish a single source of truth that all stakeholders can trust and rely on for risk reporting

Like a “family tree” for your data, data lineage provides an end-to-end view of the relationships between data items across the enterprise and between the various lines of business. It simplifies the complexity of combing through vast amounts of information and provides visibility of the data’s flow through various data points from source to destination.

ASG Data Intelligence – Automated Data Intelligence and more!

For many banks, collecting data lineage is still a labor-intensive effort. The data interacts with many different platforms and applications. Often, these technologies don’t “talk” to each other, which means someone needs to manually connect the dots, using multiple spreadsheets. In addition, the models must be maintained, current and available to regulators on short notice.

To meet these challenges, automated data lineage is a must. And it’s the de facto standard that regulators expect to see. ASG Data Intelligence (ASG DI) automates a map of your technical metadata and how it is used. It describes the structure of a piece of data, its relationship to other data, and its origin, format, and use. The technical data lineage can be enriched with business semantics – definitions, policies, rules, privacy tags and more – providing a 360-degree view of the data for business and IT users alike.

ASG DI provides searchable repository of your metadata for users who need to understand how and where data is stored and how it can be used. Users can also document roles and responsibilities and utilize workflows to define and map data.

ASG Data Intelligence can discover data from over 260 legacy mainframe and big data sources. Our data lineage tracks the “hops” from source to target and allows you to add expertise to “stitch” gaps that can’t be automatically bridged.

Other Benefits of Data Lineage

The benefits for enterprises that incorporate data lineage as part of their data management practices include:

  • Support BI reports, analysis, AI initiatives and other data strategies with trusted data
  • Identify business rules discrepancies and redundant processes, systems, data or business rules
  • Comply with regulations, such as SOX, HIPAA and data privacy (GDPR, CCPA/CPRA and others)
  • Improve data and systems architecture
  • Uplevel change management, app modernizations, cloud migrations and new system implementation by understanding potential impacts on data


While an overhaul of this magnitude is an enormous challenge, FRTB mandates can be an excellent opportunity to enhance your risk and data management practices. With this foundation, you’re able to quickly adapt your data for changing regulatory environments and become more data-driven for better insights, faster innovation and competitive advantage.

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