Case Study
Model Governance Strategy Definition & Implementation
Data
Client
Fortune 50 Financial Services Institution
Services
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.
Solution
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 lineage, including 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.
Impact
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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