Integrate or consolidate? 2 data models for effective regulatory reporting in Luxembourg

The financial services industry is just emerging from a decade of regulatory reforms at an unprecedented scale. As they run from one regulatory requirement to the other, all banks face a common struggle: the quality of data.
accentureinsights-ifrs16-header-01.jpg

The wide variety of reports banks are required to submit has made the data challenge even more stringent and complex. To meet regulatory requirements, each has shaped its own approach to data collection based on external or internal solutions. No silver bullet solution has yet emerged. The most common problems we observe in these approaches are:

  • Redundant data collection schemes
  • Lack of data consistency
  • Data quality
  • Significant manual work (delays, inefficiencies)
  • Manual input from unstructured data sources (poor audit trail, human errors)
  • Data granularity
  • Cost of implementation and not enough flexibility in the event of new regulation

However, banks are exploring new scenarios to overcome these challenges and this article takes a closer look at two models that can help to increase regulatory reporting efficiency.

Integrative Data Model 

An integrative data model provides greater harmonization and integration of data and a closer alignment of the IT systems of the supervisory authority and the supervised entities. The best known example comes from Austria where AuRep (Austrian Reporting Services GmbH) – founded in 2014 and co-owned by seven of the largest Austrian banking groups – is interfacing with the Austrian Central Bank. 

This model runs on a common platform that functions as the central interface between banks and the central bank or regulator. It allows the sourcing, enriching and processing of data and ultimately reporting on prudential and statistical requirements. It replaces the template-based reporting method by a so-called “input-based approach” centered on data cubes. Banks deliver micro data at transactional level (contracts, loans, deposits…) in the form of “basic cubes”. Basic cubes provide an accurate, commonly shared and hence unambiguous definition of business transactions and their data attributes. They establish a harmonized database model at a very granular and consistent level. These basic cubes are then enriched via the common platform by the banks. Lastly, reports are generated in the form of multi-dimensional data cubes called “smart cubes” (such as Loan Cubes, Deposit Cubes, etc.). Smart cubes are then analyzed, signed off and transmitted to the central bank.

The integrative data model represents a paradigm shift in banking supervision and statistical data remittance. And while it undoubtedly increases transparency, data quality and consistency – thus  reducing redundant data deliveries, burdensome ex-post corrections and regulatory enquiries – it calls for a complete rethinking and redesign of the regulatory reporting functions (both from an organizational and process perspectives) by banks and regulators alike.

Source : National bank of Austria.

Consolidated Regulatory Reporting Framework

A major observation on the current regulatory reporting frameworks across most financial institutions is the issue of fragmented data sourcing, processes and technologies. Consolidation into one single framework can create efficiencies across the entire reporting function: 

  • Data: Same data can be used to populate reports across the regulatory landscape and for internal purposes, reducing redundant activities
  • MIS/Shareholders: With more accurate reporting results, financial results can be produced faster
  • Workflow and Requirements: Straight through processing of information across all systems cna help meet reporting needs
  • Organization: Overarching controls and governance, with well communicated roles and responsibilities, help to mitigate manage
  • Technology: A seamless user experience makes it easier to investigate and manage risk, and create new information and data

Such a consolidated framework needs to be powered by effective regulatory reporting strategies. The transformation roadmap to implement one or more of these strategies needs to be tailored to each bank’s context, strategic priorities and current regulatory reporting framework. Here is an outline of these strategies followed by a table showing typical implementation cost and time scale. All strategies offer process improvements as well as resource and technology savings potential.

  • Centralized Data Repository: Introduces consolidated regulatory reporting tools that provide greater controls, automated submission, and eliminate the need for manual aggregation
  • Data Quality Management: Ensures lineage and documentation of data from report line item to source system to reduce the need for adjustment and manual processes
  • Financial Reporting Robotics: Automates as many manual processes as possible resulting in significant cost savings and elimination of many manually intensive processes
  • User Tool Reduction: Eliminates unnecessary user tools, consolidates user tools and incorporates them into a consolidated technology infrastructure
  • Process Improvement: Eliminates obsolete processes and optimizes existing processes
  • Operating Model & Governance: Establishes a dedicated regulatory reporting capability, with supporting governance processes and resources
  • Operational Dashboard: Establishes a dashboard to monitor report progress and data quality, setting the stage for continuous improvement
  • Regulatory Reporting as a Service: Ownership of the regulatory reporting capability is shifted from internal to external resources in lower cost locations, resulting in significant cost savings and efficiencies, coupled with process improvements, automation, etc.

Source: Accenture 

What to consider when choosing?

While the integrative data model is a marketplace initiative that requires aligning regulatory reporting transformation pace and process with the rest of the group, it allows for greater efficiencies. On the other hand, the consolidated framework can be tailored to the bank’s specific needs. It can also be scaled to other entities of the institution outside Luxembourg. 

Any decision to choose one or the other model should be based on a thorough cost-benefit analysis in the short, medium and long term, plus an assessment of the most fit-for-purpose solution based on each bank’s business model, size, scope, regulatory obligations and available resources. 

Want to know more about how Accenture can help your bank overcome the challenges and opportunities of regulatory reporting? Don’t hesitate to contact us for a chat!

If you missed our first two episodes of our 'Data at the Service of Regulatory Reporting' series. It's all here!

Authors: Nesrine Besbes Fatih Aslan