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Beyond the RegTech Frontier: The Rise of SupTech

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.

The need for SupTech

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:

  • Supervisors collect data from thousands of institutions. The lack of standardization in reporting data results in huge time and effort spent by regulators in cleaning up this data into a usable format.
  • Different geographies have varying regulatory taxonomies, which results in disparate data sets across geographies. This lack of standardization makes stress testing complicated.
  • The growing emphasis on the re-engineering of data functions indicates that in the coming years, regulators will receive more mature and complex architecture data from FIs, which the current data collection systems may be unable to respond to effectively.
  • With newer business models in FinTech on the verge of being regulated, there will be increased data inflow with newer risk parameters and variables, thus introducing newer complexities.
  • Risks today emanate from many more sources and need to be evaluated on a real-time basis to prevent them from having systemic effects. The current processes do not have provisions for such capabilities at regulator’s end.

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.

SupTech as an attractive value proposition

  • Improved Data Collection: Standardized, machine-readable, input-based approach for reporting will enable automated, streamlined data collection, thus reducing costs and time, while presenting a holistic view of risk data. The tech integration allows regulators to customize the reporting requirements and pave the way for real-time, granular data collection.
  • Productivity & Efficiency Gains: With automation reducing the need for human supervision in data clean-up and collection processes, the limited resources can be allocated to high-risk-decision making functions, thus driving productivity.
  • Proactive, Real-Time Risk Mitigation: A real-time monitoring system will allow regulators to pre-emptively identify and prevent a violation by a trader/employee, thus improving the accuracy of surveillance systems, as well as reduce misconduct.
  • Insights from Unstructured Data: Supervisors can use advanced analytics to collect and analyze unstructured data with greater efficiency. This also reduces the standardization burden on regulated entities.
  • Remote, Digital Audits Driving Enhanced Engagement: Digitized, remote audits, as opposed to on-site physical audits can reduce the compliance costs significantly while increasing the frequency and engagement between regulators and institutions.

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.

Early adopters of SupTech by supervisory agencies

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.

Challenges faced by SupTech

  • Legacy infrastructure poses one of the biggest challenges, as the outdated reporting portals and data collection systems make technology implementation a tedious, time-consuming task. However, non-invasive, modern SupTech solutions can do the job.
  • Over-reliance on automation and digital exposure can aggravate the risk of cyberattacks and operational failure, which can prove to be costly, considering the criticality of data which regulators deal with.
  • The quality of predictions and intelligence generated by the newer technologies needs to be monitored and fine-tuned with the help of human intelligence. There is a strong need to identify the right mix of automation and human supervision for next-gen supervisory functions.

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.

Future-focused initiatives by 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.

Machine-readable regulations and other SupTech initiatives by the FCA

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:

  • Project Maison: Collaboration with R3, RBS, and another global bank to explore the use of DLT in regulatory reporting. After a successful first phase, a plot with live data is in progress.
  • SmartReg: Collaboration with UCL and Santander to use smart contracts and DLT to verify compliance.
  • Ascent Experiment: The FCA collaborated with CBA, ING and Pinsent Masons to test the possibilities of using NLP and AI to interpret MiFID-II regulations, and automatically build and manage a compliance program.
  • Intelligent Regulatory Advisor: After integrating search and tagging features in its regulatory handbook, The FCA is now working on building an AI-based intelligent front-end advisor to guide an applicant through the handbook by providing basic automated advice.

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.


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