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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
    • Terms and Conditions of Use
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  1. Background

Possible Solutions

PreviousChallenges of Regulatory ReportingNextDigital Reporting and Granular Data

Last updated 3 years ago

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To meet these challenges in the digital age, authorities could benefit from “on demand” access to timely and integrated sources of data to help support and inform their supervisory assessments. Several possible solutions are therefore explored in this project.

Solutions

Descriptions

​​ Granular Data

The collection of granular data from reporting entities could replace the need for authorities to request information using templates. It could also enable authorities to reuse those data for different use cases. Supervisory metrics could also be derived using granular data, as opposed to requiring reporting entities to aggregate the required data prior to submission.

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​​ Common Data Models

Differences in the description of data for similar products and transactions across banks can be addressed using data standards and common data models. 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 data can be reported. Supervisory metrics could then be derived using programmable rules that reference machine readable and machine executable common data models.

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​​ Real-Time Information

Real-time insights using advanced analytics could be derived from large volumes of unstructured data that would supplement the granular reporting available. This would provide supervisors with additional indicators and early warnings of at-risk exposures of reporting entities.

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​​ Integration of Structured & Unstructured Data

Integrating granular data from reporting entities with other sources of unstructured information such as news and market data onto the same platform means supervisors would not have to spend time manually scanning for information. Advanced analytics such as artificial intelligence and machine learning could be used to make risk correlations and analyse sentiment, alerting supervisors in real time of issues that may need further investigation.

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