FINCONET Issues Report on SupTech Tools for Market Conduct Supervisors
The International Financial Consumer Protection Organisation (aka FinCoNet) issued its report on SupTech Tools For Market Conduct Supervisors at its virtual annual general meeting in November 2020. FinCoNet is an international organisation of supervisory authorities which have responsibility for financial consumer protection. FinCoNet promotes sound market conduct and strong consumer protection through efficient and effective financial market conduct supervision. The Financial Consumer Agency of Canada (FCAC) and the Authorité des marchés financiers du Québec (AMF) are members of FinCoNet.
The FinCoNet report comes on the heels of the Financial Stability Board’s own report, discussed in a prior post, on the use of SupTech and RegTech by authorities and institutions and presents the findings of a survey its administered to 21 market conduct authorities around the globe. The survey included questions related to cutting-edge technologies that enhance supervision as well as questions on new ways traditional technologies could be leveraged to facilitate more innovative supervisory approaches. The report points out that, in an environment where financial products and services are becoming increasingly digitized, supervisory authorities have great interest in keeping pace and evolving their own oversight activities towards the use of digital technology if they are to remain relevant.
A Strategy Can Be Helpful
Most of the responding jurisdictions indicate that careful planning, including clearly defining objectives and expectations, is a prerequisite to embarking on the SupTech journey. In addition, challenges, such as data issues and the availability of resources and necessary skills, should be addressed through the creation of a rigorous framework. Respondents are also of the view that a formal SupTech strategy can be helpful to guide the project, mitigate implementation risk and connect to strategic objectives. It is noted, however, that the mere existence of a SupTech strategy is not a predictor of a successful program. Other respondents indicate having taken a “bottom-up” approach, rather than a formal strategic approach, to ensure sustained flexibility. Regardless of the approach taken, respondents are aligned with the notion that the development of SupTech programs should be understood to present continuous opportunities for improvement and re-use.
The most frequent use of SupTech technologies are those linked to the collection and analysis of data, with more traditional tools used to process structured data, while more innovative solutions are increasingly used to process unstructured data.
“Workflow” SupTech tools are also used to automate workflows and connect various sources of data and better and more quickly manage communications among stakeholders and end-users (e.g. consumers, supervisors and regulated entities).
Finally, most supervisors say they access relevant information about the regulated entities and their business conduct through integrated databases to facilitate risk-ratings and automated warnings.
The most common issues reported by participating authorities are typically encountered during the solution implementations phase and involve data, strategic approaches and technological complexities. Data is, however, the most frequently cited challenge, because of its availability, quality, standardization and representativeness. Respondents also mention resource constraints and the lack of specialized skills as additional challenges.
A total of 37 SupTech tools are currently being used by the 21 responding supervisors. While more traditional tools are used to process structured data, more innovative solutions increasingly involve the collection and analysis of unstructured data.
(a) Structured data: e-reporting
Supervisors are developing SupTech tools to reduce or even eliminate routine manual tasks related to the treatment of large volumes of information usually received through regulatory reporting. They are also using these solutions to improve the quality of their data by eliminating the potential for human error.
The AMF in Quebec currently uses a market surveillance tool powered by artificial intelligence and machine learning to assist with monitoring systemic risk in the derivatives market. The tool was developed to automate data collection, analyze and visualize data and integrate its databases.
(b) Unstructured data: web-scraping and social media monitoring
An increasing number of supervisors are using web-scraping and media monitoring applications to conduct their oversight activities. The use of machine learning-based systems and applications, specifically text mining applications, are being used by supervisors to identify potential violations of advertising rules, identify key issues of concern for consumers and even predict potential harm to consumers in real-time.
(c) Unstructured data analytics: Natural Language Processing (NLP) Text Mining and Topic Modelling
Supervisors also report using NLP-based topic modelling tools to quickly analyze large volumes of data (e.g. consumer complaints, board records, etc.) with a view to identifying patterns and potentially harmful consumer practices.
NLP provides mechanisms to read or hear language and perform tasks such as identifying relevant topics and summarizing text. It also makes market supervision simpler by automating and accelerating the analysis of information contained in non-structured text, thus allowing the supervisor to pivot from a manual analysis of sample data to the automated analysis of a complete data set.
Here at home, the AMF has developed a tool on NLP Topic Modelling to extract and organize topics by importance from large volumes of data which allows it to prioritize the areas of its oversight focus.
Some authorities have implemented workflow SupTech tools to streamline internal processes, improve efficiencies and enhance the collection and analysis of structured and unstructured data. Workflow tools are typically used, for example, to connect multiple sources of information and manage communication among consumers, supervisor and supervised entities more quickly.
This solution is also frequently used to automate administrative processes and routine tasks such as complaints handling and on and off-site surveillance.
(e) Risk profiles
Responding authorities report using tools that not only perform functions such as data collection and data analysis, but also help create risk profiles for regulated institutions and flag early warning signs. In addition, these tools are typically capable of quickly carrying out analyses on large numbers of regulated entities to provide a snapshot of the state of marketplace in a short period of time. In Canada, the FCAC recently introduced its Market Conduct Profile (MCP) tool, which enhances its ability to assess an institution’s market conduct risk. It has also created a tool that allows for the collection, organization and storage of institution-related information.
The AMF, for its part, developed an off-site supervision reporting tool which uses a set of risk indicators and warnings to support its offsite oversight activities.
The report concludes by stressing the need for market conduct supervisors to adapt to the changing landscape caused by digitalisation. It suggests that one of the ways to achieve this is by embracing SupTech – which will add efficiencies to oversight activities and thereby ensure consumers are even more adequately protected. Finally, the report signals that a further review a few years from now will be necessary to more fulsomely assess the success of SupTech programs.
For more information about our firm’s Fintech expertise, please see our Fintech group’s page.
 Financial Stability Board (FSB) is an international body aimed at promoting international financial stability through developing strong regulatory, supervisory and other financial sector policies. They coordinate national financial authorities and international standard-setting bodies from across 25 countries
 Australia, Brazil, Canada (FCAC), Canada (AMF), Estonia, Germany, Greece, Hong Kong, Indonesia, Ireland, Italy, Japan, Mauritius, Nigeria, Peru, Portugal, Russia, Slovakia, South Africa, Spain, United Kingdom