Case Study

Model Governance Strategy Definition & Implementation



Fortune 50 Financial Services Institution


Data Management & Governance, Data Quality & Lineage

Project Overview

The Client had recent challenges meeting regulatory requirements for Model and Data Governance. ​ The financial institution engaged Reference Point to assist in the upgrade of the forecasting engine that calculated Liquidity Coverage Ratios and Net Stable Funding Ratios. A secondary effort focused on establishing foundational data & model governance to comply with regulatory and accounting needs was also in-scope.​ Reference Point liaised with key client stakeholders to define roles and responsibilities to establish a formal team structure and governance process. Additionally, RP captured data requirements and documented data lineage and transformations in support of the forecasting engine upgrade. ​


  • 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 data stewards, data owners, data custodians, and data consumers of forecasting data.

  • Additionally, the team leveraged Reference Point’s expert network to co-create a point-of-view on data domains that was tailored to the Client’s specific requirements for loans and securities.

  • Data lineageincluding data hops and transformations, was captured for high-priority forecasting data elements including source inputs, intermediates, outputs, and mnemonics.

  • The team also created and deployed data quality rules and ETL data controls to validate data transfers from source to target.


  • With Reference Point’s help, the Client was able to prioritize key data assets and improve data quality, which resulted in a re-established confidence in the accuracy of information reported internally and to regulators and enabled the Client to obtain an adequate understanding of how data is sourced, transformed, and reported.

  • The overall effort led to sufficient data quality and data movement controls over data that has critical impact on supporting business decisions and fulfilling regulatory requirements.

  • Additionally, defined governance priorities across various workstreams enabled the Client to successfully manage overall ecosystem transformation.

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