Turning data challenges into opportunities

With central banks becoming increasingly reliant on data, BearingPoint elaborates on the increasing role of innovative regulatory and supervisory technology in supervision.

Safeguarding financial stability and ensuring the fairness and efficiency of markets are primary responsibilities of financial regulators. Since the onset of the global financial crisis of 2007/2008, supervisory authorities have introduced a variety of new banking regulations at global, regional and domestic levels, and significantly tightened existing rules. With each policy development there is a need for additional monitoring; and so the regulatory landscape continues to change.

There is also an increasing interconnectedness between different types of financial sector regulations. For example, changes to international accounting standards – implemented as International Financial Reporting Standard 9 in the European Union – affect most financial institutions and their regulators in some way, across insurance, banking and capital markets. 

It is clear that regulators need to keep track of all system changes and be able to adapt within a reasonable timeframe.

 

Challenges and opportunities in data

To carry out their functions, central banks and other supervisors rely heavily on data, which is the foundation of any regulatory framework. This data – supervisory, statistical, markets data, big data, data gathered from internal and external sources, including financial institutions, agencies, governments, statutory bodies, directly or from the internet – is vast and increases each year. 

Supervisors must collect, verify, aggregate, process and analyse data in a timely manner to manage financial stability risks properly and use these insights in policymaking and supervisory processes. As well as having lots of data, being confident it is usable, accessible, fit for purpose and understandable – and making sense of it – is the primary challenge for regulators today. It is also crucial to ensure good data quality, which is no easy task, especially when the data available is in different formats and at different levels of granularity. 

Furthermore, handling and disseminating data within regulators often involves interdependencies between many internal systems across different departments. Regulators thus need to align their data management processes with their other internal business activities. They also need to balance their internal resources and expertise to absorb and handle vast quantities of data on one hand, and take action in response on the other.

In addition, regulators are often publicly accountable for the efficiency and effectiveness of their budgets. Costs of supervisory data management – and value for money – are therefore just as important for regulators as for banks, insurers and other financial institutions. 

 

The rise of suptech

In view of these challenges, there is a clear and immediate need to free up regulators’ human resources and time, and reduce costs. To do so, central banks and other supervisors must have a closer look at their IT and operational infrastructure, and reshape their data collection and management processes. As Professor Claudia Buch, vice-president of the Deutsche Bundesbank, says: “New technologies can contribute to a better quality of data and better analytical work [at the central bank].”

There are ongoing discussions about and research on different approaches to supervision and the role of new technologies. The results of a study by the Consultative Group to Assist the Poor (CGAP) reveal the costs of regulatory reporting depend more on the data collection mechanism than the amount of data. Reporting a small amount of data that needs to be aggregated and formatted into templates can be more expensive than reporting a larger amount of granular data through an automated process without templates. The CGAP’s working paper also explores opportunities created by innovative regulatory and supervisory technology – referred to as regtech and suptech – for digital financial services supervisors to rethink their approaches to data collection with the goal of strengthening supervision while fostering financial inclusion. The paper refers to AuRep – a cube‑based model of regulatory reporting implemented in Austria and developed as a result of co‑operation between the National Bank of Austria, the banking industry and BearingPoint – as a prominent example of an input‑based, as opposed to template-based, approach. 

This approach, also known as ‘shared utilities’, has caught the attention of the World Bank and the Global Partnership for Financial Inclusion (GPFI). The GFPI published a report, which highlights this approach as notable for its conceptual leadership and future‑orientation. The report sets the direction for regulatory reporting and supervision on a global and digital stage. 

Last year, the Basel Committee on Banking Supervision issued a consultation paper, which examines the benefits of regtech and suptech for the financial industry and, in particular, supervisors. The paper states that the development and application of suptech may increase efficiency and effectiveness of supervisory work, including “[near] real‑time data access”. The paper recommends regulators explore the potential of new technologies, such as artificial intelligence, machine learning, advanced data analytics and distributed ledger technology. 

A recent paper by the Financial Stability Institute of the Bank for International Settlements analyses early suptech deployment examples to assess how innovative technology can support supervision. It states that suptech “helps supervisory agencies to digitise reporting and regulatory processes, resulting in more efficient and proactive monitoring of risk and compliance at financial institutions”, and identifies two main areas of its application: data collection and data analytics.

Although different approaches are yet to be explored, it has become clear that, as reporting frameworks mature, regulatory attention is turning increasingly towards standardisation and automation of processes for handling data. This can not only help to minimise risks, reduce errors and increase transparency but also free up resources to actually use the data, thereby delivering a better basis for decision‑making. BearingPoint, as a provider of innovative regtech and suptech, supports supervisors on this journey.

 

This feature is part of the Central Banking focus report, Big data in central banks, published in association with BearingPoint.

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