Financial Risk Analytics > FRTB Solution SuiteFRTB Modellability Service

The Fundamental Review of the Trading Book (FRTB) Modellability Service combines data and flexible analytics to assess and manage risk factor modellability in both quantitative impact study (QIS) and production use cases. Enhanced transaction datasets and a dynamic, user-friendly bucketing API produce cross-asset modellability reports without loss of flexibility, control or security.


  • 95%+ coverage of CDS transactions globally

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Product Summary

FRTB establishes strict criteria for determining risk factor modellability and introduces significant capital charges for non-modellable risk factors (NMRFs). The FRTB Modellability Service helps banks satisfy the requirements and avoid punitive capital charges by transforming raw market data into compliant risk factors.

The service is comprised of two distinct products:

- Markit Risk Factor Utility (RFU), a cloud-based risk factor modelling environment

- Markit FRTB Data Service, a repository of transaction data and other FRTB-related data

As part of the FRTB Modellability Service, the RFU is prepopulated with transaction data from the FRTB Data Service. Banks have the option of supplementing this data with proprietary and third-party data.

The FRTB Modellability Service supports the derivation of modellable risk factors (MRFs) and NMRFs by counting transaction observations and assigning transactions to buckets of risk factors of varying size. It reduces the number of capital-intensive NMRFs by proving modellability.

Reports produced by the FRTB Modellability Service can be leveraged by downstream systems, including IHS Markit’s FRTB Scenario Service, to generate FRTB-compliant scenarios.

    Key Benefits

    • Unique transaction datasets

      The FRTB Modellability Service comes pre-populated with data from the FRTB Data Service, which combines data from the industry-leading MarkitSERV trade processing platform and trade data contributed by leading banks. These sources create extensive and unique transaction datasets across asset classes, which can significantly reduce NMRFs. Banks have the option of supplementing this data with proprietary and third-party transaction data.

    • Dynamic bucketing API

      A dynamic risk factor bucketing API enables users to assess cross-asset modellability in a fully-documented, user-friendly environment. Banks enjoy all of the benefits of mutualisation without loss of control, flexibility or security.

    • Out-of-the-box risk factor methodology

      Banks have the option of implementing their own risk factor definitions or leveraging the Markit Modellability Model (M3), an out-of-the-box risk factor methodology designed by IHS Markit and Oliver Wyman to highlight which risk factors satisfy the FRTB modellability requirements. This templated methodology is built into the RFU, allowing banks to avoid time-consuming and costly development work and methodology justification.

    • Data management in the cloud

      The FRTB Modellability Service is hosted by IHS Markit, giving banks access to the latest data science and technologies while reducing time to market and mutualising costs. Users can access a wide range of analytical tools. The cloud-based design offers banks the advantages of anonymity, security and mutualisation.


    FRTB: Modellability in focus

    As banks prepare for the implementation of FRTB, the impact of the new modellability requirements often appears to be underestimated.

    In this webinar, experts from IHS Markit and Oliver Wyman discuss the challenges presented by the modellability rules and share their views on best practices for achieving compliance.

    The topics covered include data pooling; risk factor bucketing; mapping transaction types to risk factors; NMRF proxy choices; desk structure and P&L attribution.