As the regulatory environment becomes increasingly stringent, financial institutions are pressed to elevate the precision and granularity of their reporting processes. This evolution in financial reporting not only aims to enhance transparency but also to empower regulatory bodies and institutions with more detailed control and insights into data.

Navigating the Complexity of Regulatory Reporting:

With the advancement of regulations, financial institutions are required to dissect their financial data meticulously, akin to surgeons in the operating room. This involves extracting detailed data, ensuring accurate mappings, and aggregating information to meet stringent regulatory reporting requirements. Such precision is essential as even minor errors can compromise the integrity of a report.

Current Pain Points:

Cost of Compliance: Financial entities face growing regulatory demands, evident in frameworks like the RBI's Automated Data Flow (ADF) and the EU's Integrated Reporting Framework (IReF). These regulations mandate extensive reporting, often overwhelming financial institutions and impacting their operational efficiency.

Data Integrity and Decision-Making: Traditional reporting methods are fraught with risks of inaccuracies due to manual data handling. However, modern solutions leveraging cloud computing and artificial intelligence have begun to reshape decision-making processes, moving away from rigid templates to more dynamic, data-driven frameworks.

Demand for Deeper Insights: There is an increasing need for real-time data analysis and the ability to respond swiftly to financial events. Improved data governance and uniform data standards can streamline reporting procedures, reduce manual labor, and provide deeper insights into financial operations.

Granular Data Reporting: The New Norm:

In response to these challenges, many regulatory bodies are overhauling their reporting standards to require transaction-level data instead of aggregated summaries. This shift enables regulators to examine data in its most granular form, enhancing the monitoring and governance capabilities and facilitating the early detection of risks.

The key benefits of such an approach would be:

• Data at the most detailed level permits versatile analysis rather than constrained viewing through inflexible reports while allowing innumerous slicing and dicing options
• By removing complicated processing steps from report creation, the frequency of data quality problems and reporting mistakes can be greatly decreased
• Regulators could more swiftly effect changes to reports and KPIs through new regulations without depending on financial institutions that are encumbered by intricate and outdated IT systems
• Leading to improved governance and oversight, enabling the early detection of systemic risks through the deployment of superior computing infrastructure and proactive use of advanced AI/ML strategies
• Reporting entities will gain from automated data collection thereby increasing operational efficiency and reduction in operation risks. With automation insights can be available in real time instead of current fixed schedule modes
• Thanks to uniform data classifications, information can be more easily exchanged among regulatory bodies, facilitating the identification of worldwide trends and possible risks that are global in nature.

Granular Data Reporting - Road Ahead & Technology Outlook:

Various international regulatory bodies, including those in Hong Kong, Thailand, Saudi Arabia, India, China, and Europe, are at the forefront of implementing granular reporting standards. These initiatives are crucial in standardising financial reporting and ensuring global financial stability.

The incumbent process though cumbersome and error prone has been established over years. Moving to the new architecture will mean a paradigm shift. The current final product whether in form of excel based templates or in some cases protocol encased like XBRL does allow to gloss over gaps in the reporting system. 

Adapting to the new standard where data is represented in the raw form exposes inadequacies which need to be addressed. It requires more robust data governance and data management capabilities along with well established data catalogs and end to end lineage of data.

Building and maintaining canonical models aren’t the easiest things to deal with. Such models tend to be highly detailed and specific requiring regulated entities to disclose at a more detailed level. In order to fulfil such obligation, financial institutions must be prepared to rectify any deficiencies in the primary and peripheral systems, opening up a lot of challenges.

Financial entities must allocate substantial resources to the implementation and maintenance of tech solutions that support these initiatives. In many organizations, altering existing procedures is challenging, necessitating investments in training staff on technology and new processes. With a shift towards real-time reporting, the previously acceptable margin of error in end-of-day processes will significantly diminish.

Also, with the need for such detailed transactional data, there will be a requirement to manage vastly larger amounts of data, compelling organizations to adopt technologies designed for such scales. Regulators have a critical part in this transition, as they must take a supportive and nuanced stance by easing data residency restrictions and establishing appropriate safeguards for practices like cloud adoption.

Conclusion:

The transition to granular data reporting signifies a critical evolution in financial regulatory practices. The granular data initiative, as a key part of the overarching SupTech initiatives is a step in the right direction. However, any abrupt change of direction from existing practice may prove counterproductive. While there is no doubt that the initiative would yield substantial benefits, the immediate Capex demand on regulated entities would be high.

It would be wise to adopt a phased approach, starting with the most accessible improvements that can provide immediate benefits and follow through with more complex architectural changes.

How Smarbl Supports Granular Data Reporting:

SmartReg, Smarbl’s flagship solution, is designed to meet the demands of modern financial reporting. Built on scalable, cloud-ready technology, SmartReg simplifies the reporting process by:

Additionally, SmartReg offers an intuitive drag-and-drop interface, empowering users to create intricate data transformations and configure end report templates and datasets with ease. This self-service capability enables businesses to address dynamic reporting needs without heavy reliance on IT support.