APRA ushers in a new era of data collection – opportunity or challenge?

“Permacrisis”, the latest portmanteau to preoccupy the world after Brexit, is essentially defined as an extended period of instability and insecurity - an appropriate description of the current state of affairs.

As 2023 begins, macroeconomic conditions remain ambiguous; rising interest rates, unstable geopolitical situations, declining global stock markets and asset classes such as housing, and inflation is at it’s highest in over four decades. Revenues and margins for organizations are under tremendous pressure. Financial services institutions (FSIs) generally, and Authorized deposit-taking institutions ( ADIs ) in particular, have their job cut out for them. In Australia, the banks may see net interest margins improve but the overall mortgage activity, the largest revenue share business, is likely to decline with increasing competition for new customers. Amidst all this, a big undertaking for the ADIs is getting ready for APRA’s direction for change in data collection with its new proposed 5-year roadmap.

Last month, the Australian Prudential Regulations Authority (APRA) published a formal response to feedback from market participants provided on the five-year data collection roadmap it laid out in March 2022.

In the March publication, APRA suggested a change in approach to data collection and created a roadmap to execute that change. The goal? To make a full transition to APRA Connect - the new data collection solution for reporting entities to lodge entity information and data with APRA - enabling data collection through the web, allowing APRA to monitor whether regulated entities are meeting their prudential standards, and ultimately decommissioning the Direct to APRA (D2A) collection system created in 2001.

APRA invited feedback and posed questions on the direction, the implementation plan, anticipated costs and benefits, and on the overall engagement in the program.

The responses from participants showed widespread alignment with the overall direction and a strong desire for all collections to be transitioned to APRA Connect. Maturity of data capabilities in data quality and data lineage were common themes with many of the respondents; however, as anticipated, the increased reporting burden and cost were a major concern among respondents.

Formal responses are archived on APRA’s website (Direction for data collections | APRA), here are a few key themes of the responses:

·        Successful implementation – The participants suggested that the APRA-regulated industry lead time for reporting should be 12 months from finalizing data collection to delivery. APRA suggested it will rather be around 9 months– showing APRA will be aggressive in the ask. A common data model with details to ensure consistency and single taxonomy will play a key role in enabling consistency and driving toward a single taxonomy. APRA aims to co-design and co-build these models.

·        Cost – There was no clear indication of the current costs but there is an anticipation for costs for APRA-regulated entities to go up in meeting the requirements of the five-year data plan. Systems, technology, process changes, data quality processes, and resubmissions will contribute to the overall increased cost.

·        Better industry engagement –The initial implementation of APRA connect, and the phase 1 implementation of the superannuation data transformation (SDT) has helped APRA realize the importance of running pilots and engaging with the industry more proactively and transparently and these will lead the way for this 5-year roadmap.

Banks will be doing most of the heavy lifting, following the guidance around capital collection, credit & liabilities collection. The design and implementation will be phased to manage the pace of change.

The following will become important for the Banks/ADI’s -

-                     Hiring the right talent for reporting

-                     Enriching the current data programs 

-                     Opportunity to strengthen the data ecosystem – e.g., Creating architectures that are flexible to adapt to changes

Today financial institutions are struggling with over 2.5 quintillion bytes of data created every day globally. FSIs produce and store a reasonable proportion of this data, and the governance, infrastructure, access, and quality issues of this data cannot be understated. Some issues like Data Infrastructure, Governance and Accountability, and Moral dilemma are a cause of concern.

-                     Data Infrastructure – Existing established data infrastructure to produce and support external and management financial reporting are not flexible and FSIs often resort to Manual workarounds and tactical approaches when regulators require a differing lens on the same data. The challenges of the existing data infrastructure quickly become apparent clearly demonstrating a lack of agility and underinvestment.

-                     Governance and Accountability Data governance and accountability models remain an evolving challenge for Australian FSI. Five to ten years ago saw the emergence of Chief Data Officers but these have generally been retired as a concept. There are disconnects between data governance policy and strategic intents and the ownership at a practical level, where shortcuts and pragmatism can overrule a strategic or policy intent. Enabling alignment of governance, accountability, and cost/benefit assessments is an ongoing challenge for FSIs.

-                     Moral dilemma FSIs have a moral challenge at the heart of their data which is whether, and how to appropriately monetize data. The unravelling of certain social media companies through the abuse of customer data demonstrates the challenge FSIs face. There is the counterchallenge of changing the expectations of customers in the new normal regarding the usage of data. Open Banking is a pertinent example of data being used to expedite approval processes and could be used for differentiated pricing or customer segmentation.

Here are some solutions to reflect on-

-                     Golden Data Sources: Significant investment has been made by Australian FSIs in data lakes, golden sources, and data schemas. However, if these are not on the cloud and are focused on gathering and housing existing known data rather than tackling the more challenging known unknown data sources, there will always be a mistrust of these data lakes as genuine golden data sources

-                     Service-oriented models: Re-defining data as a support mechanism to achieve both internal and external service standards is a powerful reframing of the reason for investment and structure of data. Data currently is often referenced from the perspective as an ‘asset’ on a ‘platform’ or in a ‘lake’ rather than a mechanism for the FSI to make more informed decisions and support the aims of regulators in achieving their aims. Regulatory reporting is often designed as a low-cost, minimum compliance approach, rather than as a critical service that FSIs provide to regulators to enable regulation to be achieved for the betterment of all stakeholders

-                     Data Mesh Architecture: Whilst there are undoubted benefits from data lakes, the need for data mesh architecture is also of increasing importance in meeting regulatory reporting expectations. The Royal Commission provided a critical example of the challenge and cost of gathering, assessing, synthesizing, and presenting FSI data, even when the data is readily accessible. The term data mesh is based on four fundamental principles that bundle well-known concepts: of domain ownership; data as a product; self-serve data infrastructure platform and federated governance

It is interesting to see how Genpact can support the assessment of these problem statements and the applicability of potential solutions to achieving the current regulatory reporting, and data needs for the organization and enabling future-proofing of the data governance, architecture, and infrastructure.

Check out a recent research done by Genpact along with Corinium (Data-driven) – a leading research firm to examine the state of data-driven business transformation. We interviewed over 200 analytics and cloud leaders and only 21% of leaders surveyed said that their enterprises use data-driven decisions to realize business value “all the time”. The research identifies three common factors that distinguish the most data-driven enterprises from those at the start of their transformation journeys:

-                     Collaboration between business and technology leaders, plus support for transformation from the company executives

-                     A culture in which data and analytics are combined with business objectives

-                     A trusted foundation of data that can be used by broader employees to uncover valuable insights

When asked “What is driving demand for data-driven business transformation in your organization?” Pressure from competitors is the most frequently cited driver for data-driven business transformation among the executives we surveyed. A full 55% say this is driving demand for transformation in their enterprises. And a major 43% say regulatory requirements are a key transformation driver.

In the current constrained environment, it will be extremely important for entities to deliberately make progress on the readiness for the data collection roadmap and requirements. Get ahead, do not leave it to the end – prioritize and align data collection readiness with key business objectives and turn this undertaking into an opportunity and differentiator for your organization. 

Co-authored with Philip Doran