February 20, 2018
With technology companies opening new opportunities in the banking and insurance industries, it’s never been more important to understand the risks they carry along. Innovative use of data – sources and analytical capabilities – bring up the question of ownership and privacy, the changing underlying technology, the entrance of new types of players with unconventional business models, and introductions of new products and services.
The development and adoption of advanced data analytics capabilities made it possible to explore new revenue avenues. Toronto Centre characterizes the data-intensive era through three V’s: high velocity, large variety, and big volumes of data. With that, it also brings raising concerns for data protection and privacy, bank secrecy, cybersecurity, and data management.
For instance, how to enforce customer consent requirements when any type of data can be used, even if its original collection had nothing to do with financial services (e.g., likes on Facebook, Google maps locations), or when financial transactions are seamless and automatized in the IoT? Are regulations on cross-border data flows adequate for the IoT and cloud computing? What constitutes personal data in this context, and who should own it: customers or the entities collecting the data? – FinTech, RegTech, and SupTech: What They Mean for Financial Supervision, Toronto Centre
With technology changing the financial services industry, regulatory authorities are forced to find new ways to understand and ensure positive implications and long-term sustainability of those changes. RegTech came as a solution to bring down the cost of misconduct and find a tech solution to a highly complex and expensive matter of compliance. Defined by IIF as the use of 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.
According to Toronto Centre, as in RegTech, SupTech solutions are automating and streamlining administrative and operational procedures, digitizing data and working tools, and improving data analytics.
Some financial authorities are also exploring opportunities to automate the regulatory process. Increasingly, innovations bet on an emerging revamping of financial supervision itself, a shift away from current approaches based on past data, lengthy onsite inspections and often delayed supervisory action, towards a proactive, forward-looking supervision that relies on better data collection and sophisticated data analytics, and greater storage and mobility capacity such as by using cloud computing.
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.
The World Bank describes an interesting example of how SupTech is being embraced by a whole nation – Rwanda. According to the World Bank, Rwanda has an ambitious financial inclusion agenda and also epitomizes a data-driven culture; as a result, there is an almost insatiable demand for accurate, high-frequency data to monitor financial inclusion progress. And, since the establishment of over 400 Savings and Credit Cooperatives (SACCOs) in 2009 and the entry of mobile network operators into the financial services space beginning in 2010, the National Bank of Rwanda (BNR) has had to significantly expand the scope of its supervisory mandate to cover new FSPs.
BNR partnered with Sunoida Solutions to develop an electronic data warehouse (EDW) system to automate and streamline the reporting processes that inform and facilitate supervision. The EDW system went live in May 2016. As of June 2016, the system covered eight banks, three microfinance institutions, two money transfer operators, and one MNO.
The EDW system is meant to generate operational efficiencies at BNR and improve the quality, frequency, and scope of reported data. For banks and other FSPs with sophisticated information systems, the EDW allows BNR to automatically pull data from their systems. This approach reduces the need for compliance officers at these institutions to manually construct and send reports, as well as the errors and inconsistencies often associated with this process. BNR pulls some types of data daily (e.g. related to transactions), greatly improving the degree to which BNR supervisors can monitor the market in real time. – The World Bank
Even Singapore is on the SupTech bandwagon with its ambition to strengthen supervision and reduce compliance costs. Singapore FinTech Journey 2.0 describes MAS embarking on its own FinTech journey to make supervision more effective and the compliance burden it imposes less painful. An example described by Ravi Menon, Managing Director of MAS, is the use of SupTech for detecting trade syndicates in the stock market.
One of the most difficult things to detect in the market is collusive behavior and price manipulation. MAS has put a data analytics and pattern recognition system in place to study trading behavior and detect accounts that may be:
acting in concert to manipulate share prices, or
engaging in circular trading to create a false impression of market interest.
Another example from the remarks is MAS working on a data analytics system to scour through the 3,000 suspicious transaction reports, or STRs, that financial institutions file each month on money laundering or terrorist financing risks.
A major source of pain for financial institutions is regulators requesting for the same data more than once under different data collection exercises.
We will fix this. By taking a more cohesive approach to data, MAS will aim to achieve zero duplication in our data requests to financial institutions. If we ask for the same data twice, the institution will be allowed to gently turn us down! We will work with the industry on making this a reality.
Today, financial institutions’ data submissions to MAS often involve manual processes to extract the information from their databases and fill up the MAS-provided form or template. And over at MAS, processing that data is also done manually. We will make this more painless. We have set a goal: all data requests from MAS will eventually be in machine-readable templates.
The requested data will flow seamlessly from financial institutions’ databases to our forms and ultimately to the supervisory dashboards of MAS officers. No more cut-and-paste. We will work with the industry on an implementation plan and a reasonable timeline. – Ravi Menon, Managing Director, MAS
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.
Use of machine learning combined with NLP can be used to identify patterns for further attention from supervisors in large and complex data. Machine learning can also be used with NLP to link trading databases to other information on market participants. This could include, for example, the ability to integrate and compare trading activity information with behavioral data like communications and to compare normal trading scenarios with those that may have substantial deviations, triggering the need for further analysis. – Artificial intelligence and machine learning in financial services: Market developments and financial stability implications, Financial Stability Board
In the paper called Sound Practices: Implications of FinTech developments for banks and bank supervisors by the Bank for International Settlements, the organization emphasizes that the aim of these initiatives is to help companies navigate the supervisory regulations applicable to fully operational financial service institutions.
While the level of support offered by each initiative varies, they all seek to provide regulatory guidance to innovative startups and incumbent firms. From the authorities’ perspective, these interactions with innovative firms add value by deepening the supervisory understanding of the risks and benefits emerging from the new technologies, products, and services, as also noted by the FSB. A proactive approach to innovation also has the benefit of helping regulatory agencies identify and explore the use of new technologies for internal supervisory purposes (SupTech). – BIS