Standard Approach for Counterparty Credit Risk (SA-CCR)

Apr 4, 2016 | Case Studies, Case Study - Data, Practice - Data

Approaches have changed as to how counterparty credit risk is measured and reported. Brickendon was engaged by a major US banking corporation to devise and rollout a standardised approach across the business and associated channels, such as QIS and ad-hoc calculators. The aim is to comply with new regulations from the Basel Committee on Banking Supervision (BCBS).

Challenges

  • The investment bank and private bank operate from different infrastructures and data sets, and consolidate only at the very end of the reporting process. These processes need to be unified
  • To create a single MI and reporting prices feed from a single consistent source
  • MI can be interrogated at a trade level. Regulatory reporting will be at a book level
  • Additional data will be required from foreign sources for the Private Banking business
  • To coordinate between the different programmes requesting new data sources
  • The Waterfall delivery approach means that only limited IT progress can be made ahead of the full requirements delivery
  • Time constraints

The Brickendon Solution

  • To create a brand new calculator with new data feeds that will replace the legacy system
  • To ascertain precisely which information is required from each department to ensure the calculator has the correct information to produce an accurate result and one that complies with all regulatory requirements
  • In order to do this, data needs to be standardised across all departments so that the quality of the information provided can be verified before being inserted into the calculator
  • To remove any margin for error in the data supply, particularly discrepancies between risk and finance data
  • To complete the development phase early enough so that the new calculator can run in parallel with the legacy system for three months prior to go-live

Client Benefits

  • Regulatory compliance to ensure capital allocations are as efficient as possible
  • A better handle on the quality of the data available within the organisation, which could in turn be used for other activities
  • Use of strategic data sources to improve the accuracy of risk calculations and facilitate management support
  • Improved data quality for KYC processes and regulatory reporting requirements

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