Case Study

Enable Sufficient Data Quality & Data Movement Controls



Fortune 50 Financial Services Institution


Data Management, Data Governance, Data Quality

Project Overview

The Client developed an in-house platform to calculate all interest rate risks for the retained liquidity portfolios, used primarily for interest rate hedging. More recently, the platform’s capabilities have been expanded to capture GAAP risks and other financial reporting for all assets and liabilities. The Client engaged Reference Point team to help upgrade the forecasting engine to report Liquidity Coverage Ratios and Net Stable Funding Ratios accurately, implement foundational data & model governance, and comply with regulatory and accounting needs.


  • Reference Point deployed a team of data experts and data management consultants, to advise on business and technical requirements for model governance strategy.
  • The team liaised with various stakeholders to establish clear roles and responsibilities, including the documentation of data stewards, data owners, data custodians, and data consumers of forecasting data.
  • Additionally, the team leveraged RP’s expert network to co-create a point-of-view on data domains that was tailored to the Client’s specific requirements related to loans and securities.
  • Data lineage (including data hops and transformations) was captured for prioritized forecasting data elements including source inputs, intermediates, outputs, and mnemonics.
  • The team created data quality rules and data movement controls and deployed them to validate data transfers from source to target.


  • Prioritized key data assets, improved data quality, re-established confidence in the accuracy of information reported internally and to regulators, and gained adequate understanding of how data is sourced, transformed, and reported.
  • Enabled sufficient data quality and data movement controls over data that has critical impact on supporting business decisions and fulfilling regulatory requirements.
  • Defined governance priorities across various workstreams to successfully manage ecosystem transformation.

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Strategy. Risk. Data. Technology. Reference Point evaluates critical elements all together to build cohesive, lasting value for financial services clients.

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