Granular Data Requirements March Across Asia Pacific
The shift towards granular data reporting (GDR) in Asia-Pacific represents a significant evolution in the regulatory landscape in the region, while also reflecting the global trend towards data-driven oversight and enhanced transparency. The European Central Bank continues to standardize and harmonize reporting requirements and reduce the reporting burden for banks with the AnaCredit implementation and Integrated Reporting Framework (IReF) roll-out.
Countries in the Asia Pacific are at various stages of implementation, with some leading the charge with comprehensive transformation programs away from traditional aggregated reporting to granular data collection, including:
- Australia: APRA introduced Comprehensive Data Collection (CDC) to gather richer, more granular data, which is expected to improve data-driven decisions, optimize total cost of ownership (TCO), and increase operational efficiencies.
- Japan: Bank of Japan (BOJ) and Japan Financial Services Agency (JFSA) have taken steps to eliminate overlaps in their existing supervisory and reporting frameworks with a data-centric, common data platform.
- Hong Kong: Hong Kong Monetary Authority (HKMA) implemented GDR to support improved surveillance and policy formation.
- India: The Reserve Bank of India (RBI) established the Centralized Information Management System (CIMS) to automate regulatory and supervisory submissions, replacing the existing XBRL system.
- Philippines: Bangko Sentral ng Pilipinas (BSP) rolled out FRP v15 XML Consolidation and the International Transactions Reporting System (ITRS) to monitor cross-border transactions and compile balance of payments statistics.
- Singapore: The Monetary Authority of Singapore (MAS)’s Data Collection Gateway (DCG) enhances data quality and streamlines submission.
This shift is not without its challenges and both financial institutions and regulatory bodies face hurdles. However, the lessons learned from these early adopters – who highlight the importance of strategic planning, technology investment, and collaboration between regulators, Reg Tech, and banks to ensure a smooth transition – are invaluable. By adopting best practices from these experiences, regulators and banks can mitigate risks and capitalize on the benefits of GDR, such as improved surveillance capabilities, enhanced compliance, and operational efficiencies. As institutions in the region make progress, it is evident that GDR plays a pivotal role in shaping the future of financial regulation and decision-making processes.
Global Data Reporting: Key Milestones and Challenges
Both Hong Kong and Japan have hit key milestones. Similar to an earlier experiment by the HKMA, JFSA conducted research using Machine Learning on time-series data from Credit Risk Information Integration Service (CRITS®) and other sources to measure explanatory variables for downgrading borrower classification from “normal” to “needs attention” or below and predict the downgrade probability in less than one year.
Common Challenges for Banks and Regulators
As more Asia-Pacific jurisdictions develop GDR requirements, reporting firms experience functional and technical challenges as they work to conform with complex regulations like Basel IV. In transitioning to GDR, banks and regulators will need to tackle the following common issues:
Bank Challenges
- Need for centralized, clean data sources required across all types of risk which are being measured in the Basel framework to ensure accuracy in the estimation of capital, liquidity, and leverage ratio variances.
- Increased granularity of adjustments changes from aggregated to granular as well as in the number of records and data fields for adjustments.
- Sophisticated data-attestation and sign-off processes by product teams close to the origination of data, at the early stage of the workflow before, reporting team processes and submits the data.
- Ability to identify and submit a subset of data to meet delta submission and restatements.
- Changes in submission formats (APL/XML, etc.).
- Tokenization and encryption requirements to mask client-sensitive information.
- Data lineage to understand end-to-end flow to prove data accuracy and detect and investigate anomalies.
Regulator Challenges
- Technical challenges to efficiently process and build analytics on a large volume of data.
- Ability within supervisory teams to analyze a single source of data instead of each team monitoring at a pre-defined level of aggregation in form-based reports.
- Balancing act between expanding data compliance to banks or innovating to analyze macroeconomic problems.
- Ununiform regulatory definitions and data availability across regulated-entities and regulators (even in the same jurisdiction) create complexity in data reporting and analysis.
While there are challenges in unlocking the value of data for institutions and regulators, there is also hope or more proactively, preparation.
A Granular-Data Reporting Framework: Futureproofing
Banks can avoid several key risks, including the following, by establishing a GDR framework early in their transformation process:
- Reactive scramble and higher costs: Late implementation or waiting until GDR becomes mandatory can lead to rushed and potentially flawed system implementations, incurring higher costs.
– By proactively developing a strong end-to-end data-management framework and removing current reporting silos, banks can have a well-defined plan for data collection, storage, and reporting. This approach allows for more efficient use of limited resources.
- Data-quality issues and regulatory scrutiny: Without a GDR framework, banks are prone to inconsistent or inaccurate data spread across various systems. Cleaning this data becomes a significant burden when facing mandatory reporting deadlines.
– An early focus on GDR allows banks to implement data-quality checks and cleansing processes. This ensures the accuracy and consistency of reported data, minimizing the risk of regulatory scrutiny due to data quality issues.
- Regulatory friction and potential penalties: A rushed implementation can lead to compliance gaps, increasing the risk of regulatory penalties.
– By integrating a GDR framework, banks ensure that they have the systems and processes to comply with reporting requirements from the outset. This smooths the transition with regulators and reduces the likelihood of non-compliance and/or penalties.
- Missed opportunity to enhance risk management: Without a GDR framework, banks must rely on less granular data for assessment, limiting their ability to proactively identify and address potential risks.
– A GDR framework provides banks with access to a richer data pool. By analyzing this data, banks gain deeper, data-driven insights into their risk profile for more effective risk-management strategies.
- Competitive disadvantage and reputational risk: Banks that are slow to adapt to GDR risk lagging behind competitors who have a robust framework.
– An early focus on a GDR framework demonstrates a bank’s commitment to compliance and data-driven decision-making. This can enhance their reputation within the market and attract investors who value transparency and risk management.
In other words, they can take key steps that facilitate a successful GDR compliance
- Collect, consolidate, and cleanse data at the most granular level.
- Centralize regulatory classifications and data enrichment.
- Gain stakeholders’ confidence by instituting the correct reconciliation and analytics framework.
- Transition from siloed top-down reporting to data-driven bottom-up reporting.
- Establish an operating model with the necessary workflows and user-access management.
Additionally, fostering a culture of data literacy and collaboration across departments is essential for ensuring that GDR becomes an integral part of the bank's operational processes.
By deploying the appropriate GDR framework, instead of tactical solutions that simply address regulatory requirements, banks can smoothly navigate the transition, avoid costly missteps, and unlock the GDR benefits for improved risk management, regulatory efficiency, and a stronger market position. Not to mention, futureproof themselves for the next inevitable rounds of change.
