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  • Ellipse Overview
  • Phases of Ellipse
  • Background
    • Challenges of Regulatory Reporting
    • Possible Solutions
    • Digital Reporting and Granular Data
    • Understanding Data Needs of Stakeholders
  • Ellipse Phase 1: Proof of Concept
    • Two Jurisdictions, One Common Data Model
    • Cross-Border Data Model using Retail Mortgage Loans
    • Data Components for Retail Mortgages
    • Data Attributes
    • Data Definitions
    • Using the Common Domain Model (CDM)
    • Normalising Common Components
    • CDM Mortgage JSON
    • Programmable Reporting Logic and Machine Executable Reports
    • VIDEO: Demonstration of the Mortgage CDM
    • Our Findings
    • Next Steps
  • Annex
    • Terminology & Acronyms
    • References
  • About
    • Contact the Ellipse Team
  • LEGAL
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  1. Background

Digital Reporting and Granular Data

PreviousPossible SolutionsNextUnderstanding Data Needs of Stakeholders

Last updated 3 years ago

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Data-driven insights start with large quantities of structured granular data. A key component of an Ellipse-type platform that would enable supervisors to be data-driven is a system for the digitally enabled collection and processing of granular and standardised data. This means that those data are:

  • Consistently understood by all stakeholders

  • Can be repurposed for different use cases

  • Represented in such a way that allow programmable code to reference those data to generate the reporting of regulatory metrics.

A number of regulatory authorities have been exploring different ways in which to replace template-based, aggregated regulatory reporting with granular data and digital regulatory reporting []. The vision of regulatory reporting using granular data removes the need for multiple templates, allowing supervisors the opportunity to constantly re-purpose common data points for different analytical use cases.

However, granular reporting requires a common understanding by authorities and financial institutions of what those data are, so that financial institutions can map their operational data to a common “input” before the required reporting can be generated. Important initiatives around data standards, taxonomies and data models are being developed for this purpose []. However, data standardisation more generally remains nascent globally.

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