September 5, 2019
Certain events, over the past decade, sparked a far-ranging overhaul of the international architecture for financial regulation, coupled with a deep reflection around the fitness-for-purpose and effectiveness of supervisory efforts. For instance, in response to the financial crisis that began in 2007, achieving transparency within the financial sector accounted for nearly 20% of the 2009 Group of 20 action plan. Since then, hundreds of rules have been written and – with more than 30 new reporting regimes coming down the pipeline to 2020 and beyond – firms and regulators continue to commit significant resources to implement the ongoing change requests on top of managing existing obligations.
Consider, for example, that over the last 10 years or so, regulators have demanded more information and tougher compliance requirements from the organizations they oversee. Whether the financial crisis, the Panama Papers or AML lapses, there is a deep imperative for regulators to ensure that obliged entities are meeting the standards enshrined in laws. Along with more regulation and more reporting comes the need for more transparent, tech, and data-driven approaches to report and monitor activities with reduced risk and errors. With new technologies to solve regulatory and compliance requirements more effectively and efficiently, RegTech has been one of the hottest investment and entrepreneurship areas, but it also has been a starting point for another area of development – supervisory technology, or SupTech – which offers an opportunity to supervisory agencies much like RegTech brings efficiency to industry regulators and reporting institutions.
In collaboration with RegTech players, regulators are increasingly using technology to digitize data, streamline their operational procedures, and automate the regulatory reporting and collection process, while also driving broad-based innovation in policymaking and regulatory strategy. In addition to operational efficiency enablement, SupTech is also driving broad-based innovation in policymaking and regulatory strategy.
The graphic above highlights the various areas of financial supervision where SupTech is being leveraged. Under data collection, the areas of SupTech applications are reporting, data management, and virtual assistance. Data analytics, market surveillance, misconduct analysis, and microprudential & macroprudential supervision are some of the verticals where SupTech is being utilized. However, while it’s important to understand how it is being utilized (and where), it’s just as important to be aware of just why it is so important today.
One of the main promises of SupTech is seen in shifting away from templates and manual procedures to support data input and data pull approaches, data accessibility, reporting utilities, making sense of unstructured data, data quality management, and, finally, regulatory submissions. Accordingly, let us take a look at some points that serve to underscore the importance of SupTech:
Having said this, there are a couple of questions that arise. For instance, how can SupTech drive next-gen supervision? And why, if it’s so effective, haven’t we witnessed the large-scale implementation of this technology, yet? We’ll get to the matter of the adoption of SupTech by regulators, but first, let us take a closer look at how this technology can enable future-ready SupTech. To do so, it’s important to understand the range of benefits it offers.
Having said this, it’s also important that we take a look at who has been taking advantage of it, especially in its nascent stage of adoption.
The table below contains some examples of the technologies currently used by supervisory agencies, including ones that are under development. We’d like to point out that the graphic is strictly indicative, based on publicly disclosed activity, and should be considered as an introductory view, rather than a complete picture of the early adoption of SupTech among regulators and other entities.
However, every innovation, regardless of its origin, use-cases or adopters, comes with its own set of challenges it needs to overcome in order to scale effectively. Accordingly, here’s a look at some of the challenges surrounding SupTech in the industry.
In spite of such challenges, it is very interesting to observe the fast-paced adoption of SupTech by regulators in large economies around the world. In this story, we take a look at two of the regulators that have been early initiators for SupTech – the FCA and the MAS.
The Monetary Authority of Singapore (MAS) is one of the first regulatory bodies to have a dedicated SupTech office as part of its data analytics group, working on several initiatives to integrate modern technology in its supervisory functions. It has also set up a data analytics and pattern recognition system in place to study trading behavior and detect collusive (including circular) trading and price manipulation.
The MAS further developed a data analytics system which enables it to scour through the 3,000 suspicious transaction reports, or STRs, filed by financial institutions for the AML/CFT risks data on a monthly basis.
The ultimate goal is to eventually make all data requests from the MAS in machine-readable templates to avoid data duplication requests. Automated data processing is the works for seamless flow to the regulatory dashboards of the MAS officers.
The regulator has also been quite vocal about its enthusiasm for SupTech. In a recent interview with MEDICI Studio, Mr. Sopnendu Mohanty, Chief FinTech Officer at the MAS, quoted: “SupTech in a way is designed around the concept of using technology for supervision. Why we did it in MAS? Because we saw the power of technology, by the banks, compliance officers using this technology called RegTech. So we thought (to) let it flip it back to regulators and see can regulators use the same data to supervise in a more efficient way.”
He further states, “With industries moving into more complex financial activity, it is hard to go sit in a space and do supervision. This is a better way to read data remotely, make some analytics around the whole data, and supervise more smartly. More importantly in Singapore last year, our governor told by 2022 all banks must submit data in a machine-readable format, and once they submit the data, we can do a better job as a regulator to regulate this industry.”
The FCA’s collaborative initiatives are driving SupTech adoption and paving the way for a next-gen regulatory system. Some of the initiatives undertaken by the FCA include:
Furthermore, in November 2017, the FCA, along with the Bank of England and other regulators, held a two-week TechSprint with an objective to explore the use of AI/ML for developing a model-driven machine-readable regulatory framework. Several banks and RegTech startups were a part of the TechSprint, and many of them presented their POCs.
Against the backdrop of such important initiatives by regulators like the MAS and the FCA, it’s interesting to take a look at what some of the other regulators around the world have also been doing with regard to SupTech.
Currently, regulatory action is often delayed as it relies on historical data and time-consuming on-site inspections. SupTech solutions can help achieve near real-time supervision by improved data collection mechanisms and faster & better interpretation through cloud & advanced data analytics capabilities.
Just as RegTech has improved the monitoring and reporting process for institutions, SupTech has the potential to help regulators improve efficiencies by standardizing reporting and streamlining data collection and analysis. The outcome could not only be lower costs for regulatory agencies, but fewer fines for institutions as cooperation between institutions and regulators improve through technological innovations.
All said and done, it must be acknowledged that SupTech is still at an early stage. Potential uses of SupTech by central banks and prudential authorities are yet to be fully discovered, developed, and adopted. Nonetheless, financial institutions are already exploring the convergence of advanced technologies (AI, deep learning, NLP, etc.) and supervisory solutions. It’s clear that even as most of the SupTech solutions, globally, are still being prototyped, we can surely expect large scale implementations in the coming years.