Draft:Supervisory Technologies (SupTech)

Supervisory Technologies (suptech) refers to the use of innovative technological solutions and data science by financial supervisory authorities to regulate and supervise financial service providers more effectively and efficiently. By leveraging advances in technology and data science – such as machine learning, natural language processing and big data analytics – suptech solutions can help supervisors to oversee financial institutions, products and services more effectively, and identify, manage and mitigate risks in the financial system. The use of suptech solutions can also help reduce the regulatory compliance burden on financial firms, improve the accuracy and timeliness of regulatory reporting, “democratize” consumer protection, and accelerate innovative data architectures to address competition issues in the digital economy.

Suptech is a critical component of the broader digital transformation of financial services and a rapidly evolving field that offers significant potential benefits to financial authorities.

According to the Cambridge SupTech Lab, over 70% of surveyed financial authorities are using suptech, mostly to support prudential supervision and consumer protection. Recent research by the Financial Stability Institute across 50 jurisdictions highlighted that over 90% of them have already deployed suptech tools – most commonly for regulatory reporting, assessing risk and automating supervisory processes.

Origin and evolution
The term “suptech” was first used by Ravi Menon (economist) in 2017. However, the use of technology is not new to financial supervision. Following the 1987 Black Monday (1987) market crash, supervisors began employing technology in their operations to improve transparency and risk management in the financial markets. In 1993, the U.S. Securities and Exchange Commission mandated electronic filing through its Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system to integrate digital disclosure requirements by supervised entities and enable monitoring by supervisors. This has evolved over time into more complex and sophisticated applications.

The Bank for International Settlements and RegTech for Regulators Accelerator (R2A) have broken down the technology and data science modernization of financial supervision into four “generations”. The framework has been updated by the Cambridge SupTech Lab. The first generation involves data management workflows with intensive manual input, and mostly delivering descriptive analytics. The second generation digitizes and automates certain manual processes in the data pipeline. The third generation covers big data architecture enabling predictive analytics whereas the fourth generation involves the addition of AI as the defining characteristic.

Benefits of SupTech and Examples of Applications
Improved Oversight: Financial authorities have deployed suptech solutions to improve their oversight of financial institutions by providing monitoring and analysis of data from multiple sources to detect and respond to risks more quickly and effectively, reducing the likelihood of systemic failures and financial crises. For example, the Australian Securities and Investments Commission employs a Market Analysis and Intelligence (MAI) platform, which collects real-time data feeds from all Australian primary (ASX) and secondary (Chi-X) capital markets for equity and equity derivatives products and transactions. The MAI platform has a real-time alert monitor that detects and identifies abnormalities in order and trade messaged in traded securities. It also contains standard reports that allow supervisors to narrow down and analyse market data in a bid to identify potential market misconduct such as insider trading and market manipulation, in trading accounts.

Enhanced Efficiency: Suptech solutions can automate many of the tasks involved in regulatory oversight, such as data collection, validation, analysis, and reporting. This can reduce the burden on financial institutions and supervisors alike, freeing up resources for other tasks. For example, the Bangko Sentral ng Pilipinas has developed a suptech solution that enable financial firms to automate their regulatory reporting by providing a standardized digital format for data submission. The program uses APIs to allow financial firms to submit their data directly to the agency, reducing the burden of manual reporting and improving the accuracy and completeness of regulatory data. By leveraging the data collected and validated through the API, the central bank can gain a more comprehensive view of the financial services market and identify potential risks to financial stability more quickly and effectively.

Better Data Quality: Suptech solutions can help improve the quality of data used in regulatory oversight by identifying errors and inconsistencies, and by providing tools for data validation and reconciliation. For example, the Bank of England implemented a cloud-based platform called RegData that automates data collection and analysis, improving the accuracy and timeliness of regulatory reporting.

Richer Datasets: Suptech solutions that utilize NLP can analyze unstructured data, such as customer feedback and social media posts, to gain insights into customer sentiment and behavior. This can help financial authorities better understand customer needs and preferences, inform policy decisions, and improve customer satisfaction. The Bangko Sentral ng Pilipinas has developed a chatbot called "Bob - BSP Online Buddy" that uses NLP technology to provide answers to frequently asked questions about the BSP and its services. The BSP Online Buddy is available 24/7 and can be accessed through the BSP website or its Facebook page, as well as by all Filipinos through their feature phones via SMS. Users can type their questions in natural language and the chatbot uses NLP to understand the question and provide an appropriate response. The chatbot can answer a wide range of questions, including those related to BSP regulations, monetary policy, exchange rates, and other financial services. It can also provide information on the BSP's history, mission, and organizational structure. One of the benefits of using NLP technology in the chatbot is that it can understand and respond to questions in different languages, including Tagalog and English, as well as Taglish (an hybrid of the two languages, where Tagalog words are often used in English sentences or vice versa, which is a form of language commonly used in everyday conversation in the Philippines, especially among younger generations and in informal settings). This makes the chatbot accessible to a wider range of users, including those who are not fluent in English.

Greater Transparency: Suptech can help promote greater transparency in the financial system by making it easier for supervisors to access and analyze data from financial institutions. This can improve the accuracy and timeliness of regulatory reporting and increase public trust in the regulatory process. For example, The Malta Financial services uses Blockchain analysis tools relying on machine learning algorithms to identify and remove the anonymity from transactions, thereby assisting regulators in assessing and overseeing the integrity of virtual asset businesses. By employing this solution, regulators are able to track and evaluate the risk exposure of virtual asset businesses, including cryptocurrency exchanges, collective investment schemes, and initial coin offerings (ICOs), in order to assess and quantify their potential exposure to risks. This SupTech solution, implemented by the MFSA, enables comprehensive monitoring of crypto businesses throughout their entire lifecycle, encompassing both pre-authorization and post-authorization stages. By leveraging machine learning and de-anonymization techniques, the MFSA can effectively monitor the activities of virtual asset businesses and ensure compliance with regulatory requirements. This not only enhances the transparency and accountability of crypto businesses but also strengthens the regulatory framework surrounding the crypto industry.

More Effective Risk Management: SupTech solutions can provide regulators with better insights into systemic risks in the financial system, enabling them to take more proactive and effective measures to mitigate these risks. For example, the European Central Bank developed an Early Warning System (EMS) to detect potential financial distress cases of the smaller European banks they supervise and allow for corrective Action. The EMS tool employs an ML technique of supervised learning and builds on a dataset consisting of bank specific variables coming from quarterly supervisory data (mainly COREP and FINREP), complemented with banking sector specific variables (e.g., whether a bank is a member of an Institution Protecting Scheme) and macro-economic indicators.