Granular Data Reporting Regulations in Asia Pacific - Vermeg
Granular Data Reporting Regulations in Asia Pacific

Granular Data Reporting Regulations in Asia Pacific

2022-02-17

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What is Granular Data Reporting (GDR)?

The regulatory compliance space is undergoing a significant transformation globally, and data granularity is at the forefront. Due to the new GDR reporting requirements, banks will be expected to submit transactional/record level data as granular and disaggregated data sets to regulators, supplement current template-based reporting requirements, which are aggregated. These data sets must conform to a data model specified by regulators.

Across all regions, there are different terminologies used for granular data reporting i.e., Digital regulatory reporting (DRR), Data model reporting, Data schedule reporting, etc.

 

Benefits of GDR for the regulator and the regulated:

  • Regulators across the globe see high-quality granular reporting as a route towards greater transparency and improved techniques to calibrate financial data. This will support improved surveillance and regulatory/ macroprudential policy formation for the regulator.
  • Data concepts are harmonized and enable better communication between the regulator and industry participants. Granular, disaggregated data sets are expected to be less ambiguous and easier to provide by the banks.
  • Reduces the regulatory data burden on the industry by minimizing duplication of data collections and reduces banks’ ad-hoc reporting burden in the long run.
  • Once GDR systems are in production, regulators will be able to perform near-real-time analysis and monitoring of data.(using API pull mechanisms etc.).

 

Granular Data Reporting Regulations in the APAC region:

 

Hong Kong Monetary Authority, HKMA GDR:

Depending on the size of the financial institutions, GDR regulations are made applicable by HKMA in phases.

  • 2019-2020 (Pilot phase with selective banks for):
    • Residential Mortgage Loans (RML)
    • Corporate Loans (CL)
  • 2021 (Expansion to cover more reporting banks for):
    • Interbank Loans (IBL)
    • Debt Securities Held (DSH)
  • 2022: (Further expansion to cover more reporting banks for):
    • Corporate Loans (CL),
    • Interbank Loans (IBL),
    • Debt Securities Held (DSH)

 

Australian Prudential Regulation Authority, APRA ARS220:

APRA plans to implement GDR regulations across ADI’s in phases:

  • ARS 220 (Credit Quality) is being implemented in the first phase. This standard is expected to replace 3 reports from the current suite of template-based reports. These new regulations will become effective from Q1’23.
  • The data collected by ARS 220 will form the basis of an ADI financial instrument data model which will be extended at a future date to include topics such as capital adequacy for credit risk amongst other areas of interest.
  • In line with current practice, APRA will consult publicly on any changes before incorporating them into any data collection. APRA is proposing a risk-based approach to reporting based on the complexity of the ADI where less sophisticated ADIs have reduced reporting requirements. This proportionate approach seeks to balance the burden with the supervisor’s requirements.

 

Bank Of Thailand:

BOT is one of the first to have introduced API submissions for data sets.

Phases:

  • Regulatory data transformation (RDT) program to be implemented from 2021 to 2023.
  • The Credit risk data set became effective from 2021, and these regulations will be gradually expanded to other reporting areas.

Other GDR regulations/white papers in Asia:

CBIRC, PBOC, SAFE (China), Reserve Bank of India (India), Bank Indonesia (Indonesia), Bangko Sentral ng Pilipinas (Philippines).

 

GDR regulations in other regions:

  • European Central bank – AnaCredit.
  • Federal Reserve (US) – FRY14, 2502A, 2590.

 

Considerations for the reporting institutions:

  • The data model requirements from regulators are very prescriptive and granular.
  • Reporting entities will have to publish a lot more data points compared to current conventional template-based reporting.
  • This also means huge volumes of data need to be submitted by the reporting entities.
  • Entities will have to prepare themselves to bridge the gaps in their upstream/core banking systems to capture these new additional data point requirements.
  • Systems, processes, and controls should be put in place by the banks to maintain the accuracy/reliability of the data, and such processes should be reviewed and tested by external auditors.
  • Regulators would like to perform near-real-time analysis and monitoring of data and the frequency of such collections can be higher than conventional template-based reporting.
  • GDR programs will be implemented in phases. During the initial phases of GDR program implementations by the regulators, the proposed GDR models are likely to keep evolving, and banks should be prepared to cope with the speed/quantity of change.

 

Future regulatory landscape?

Regulators will collect more and more granular and structured data, with regulators accepting different paces of adoption. We can expect these new data-driven reporting requirements to complement existing template-based reporting rather than completely replacing them.

 

VERMEG capabilities and expertise to help support GDR reporting obligations of the reporting entities:

Reporting institutions will benefit from using fully automated reporting solutions, such as AGILE Reporter for GDR, that can handle huge volumes of data, are scalable, and that can enable fast data processing. VERMEG has vast experience with successful GDR implementations globally, for example,  for AnaCredit and Federal Reserve requirements, and we are very well positioned to support the banks for evolving GDR requirements across the APAC region.

VERMEG’s GDR capabilities will help banks with seamless implementations to meet required GDR compliance, such as:

  • Full automation of GDR compliance requirements for end-to-end, from banks source data straight-through automated regulatory transmissions
  • Automated validations of data sets (regulator defined and user-defined) within data sets, across data sets, across the periods
  • Support for data manipulation on the data sets (edits, bulk-edits)
  • Audit workflows on edits performed on the data sets
  • Approval workflows on data sets (4 eye, 6 eye checks)
  • Data lineage across the data sets
  • Meaningful reports for reconciliation purposes
  • Support for delta submissions & period vs. period changes
  • Analytical capabilities on data sets
  • Support for different submission mechanisms, for example, XML, XBRL, CSV, API
  • Cross validations between current template-based reporting and the new GDR data sets
  • Support for both on-premises & managed cloud deployments
  • Usage of big data technologies to support huge volumes of data and fast processing

 

Pavan Kumar Pothuraju

Pavan Kumar Pothuraju, Head of Regulatory Product Management APAC.