Since the 2007-2008 financial crisis, global regulatory regimes and reporting have improved significantly, and the Basel reforms were broadly deemed sufficient. However, the widespread easing of regulatory requirements and additional ad hoc requests due to the COVID-19 crisis coupled with the high costs for financial institutions (FI) highlight that the current regulatory reporting model is not sustainable enough, especially in times of intense stress.

We have identified three major deficiencies in banking regulation:

1. The generally low application of innovative technology in the fields of digitization and modern computing:

In most regulatory frameworks in global jurisdictions, regulatory data flow still happens in a quasi-manual, template-based fashion. This means that the mere automatization of manual, printed, or handwritten reporting processes of aggregated data, which was the main activity in the past years, is not enough. 

2. The high cost of regulation and regulatory reporting:

To a large extent, the current high costs in regulatory data generation for institutions are rooted in the necessity to leverage the same information artefacts over and over again for different non-aligned regulatory reporting regimes with myriads of templates (prudential, national, statistical, granular, resolution reporting) with often very similar, but slightly differing definitions.

3. The lack of operational excellence:

For one, offsite supervisory overview is still limited due to the nature of the collected data. Aggregated and template-based reporting is conceptually more prone to data correction or even manipulation. Another problem is the lack of quality, timeliness, and inter-entity matching, meaning the complementary fit of the two datasets, representing two sides of the same transaction.

To overcome these issues, we have proposed a new approach, called RegOps, to systematically change how regulation is developed and deployed and how data is exchanged between regulators and regulated using push and pull approaches.


A regulatory reporting framework that combines an integrated data flow, a common processing of standardized, granular datasets based on a big data-enabled platform for computation and analysis.

The RegOps model

RegOps is closely connected to the term DevOps, known from software development and seen as the answer to the shortcomings of the waterfall model. Regulation has been developed (conceptualized, drafted, released) and rolled out according to the waterfall model over the decades, leading to disastrous, purely reactive time-to-market and offering hardly any flexibility in embracing regulatory change. Most of all, it created enormous costs to regulators but, more importantly, to the financial services industry. Similar to DevOps, RegOps improves the way regulators and regulated entities interact: collaboration, continuous delivery, constant feedback and communication between regulators and the regulated, while delivering regulatory change incrementally in small releases without affecting the whole system.

RegOps includes the following three essential elements:

A unified, normalized, universal data model and standardized, common regulatory logic for prudential, statistical, financial, and resolution regulatory reporting purposes
A fully integrated, bi-directional data delivery stream including a toolset to export, transform, and load data to deliver functionally valid results
A big data-enabled platform to collect, store, and analyze data to gain insights for regulators

When we combine these elements, we can see a system facilitating direct, fully integrated access of regulators to the contract-granular regulatory databases of the financial institutions via APIs from various data sources (e.g. legacy data warehouses, data lakes, distributed ledger / blockchain systems). The collected data is then validated, refined and transformed for analysis, via standardized regulatory processing & allocation logic. The mined granular data can then be flexibly accessed & visualized via BI tools in the form of existing templates and more interesting formats and is a solid foundation for the application of advanced analytics or AI approaches.

RegOps Approach - Solution Sketch

This approach helps to streamline the reporting stream as far as possible and largely solves the issues of system breaks in the current regulatory reporting flow. Furthermore, it helps to solve the issues of standardization for the data model and the data processing logic to ensure the highest possible quality and comparability.

Interested to learn more? Read the whitepaper "The Future of data collection and data management: Agile RegOps for digitalizing the regulatory vaule chain by Martina Drvar (Croatian National Bank) Dr. Johannes Turner, (Österreichische Nationalbank), Maciej Piechocki (BearingPoint RegTech), Eric Stiegeler (BearingPoint RegTech) and Daniel Münch (BearingPoint RegTech) and contact us for more information.

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