Digital technologies and data – including Artificial Intelligence (AI) – hold the potential to automate and thus improve the efficiency and effectiveness of regulatory, supervisory and enforcement activities, which have become increasingly complex in recent years. Looking at the most common uses of supervisory technology (SupTech) by competition authorities, securities regulators and anti-corruption agencies to date, the OECD’s Emeline Denis identifies associated benefits, risks and challenges, and outlines considerations for devising adequate SupTech strategies across policy areas.
Regulatory, supervisory and enforcement authorities all rely on data, internal procedures and working tools, as well as human and other resources. One of the common challenges they all face – albeit to varying degrees – relates to low data quality and time-consuming manual procedures. These challenges are magnified by the increasing volume and frequency of both structured and unstructured data that these authorities need to process. SupTech applications – including those relying on AI – can help authorities address these challenges by automating routine tasks, thus allowing them to focus on activities that require human judgment and expertise, better allocate human resources and reduce costs over time.
SupTech applications evolve along with technological innovations. AI belongs to the latest generation, enabling prescriptive analytics. While AI-based solutions potentially yield the most value for authorities by enabling forward-looking supervision, the use of AI-based applications by supervisory authorities is still at a relatively new and sometimes experimental stage. However, earlier and “less advanced” technologies can still generate sufficient information and substantial efficiency gains to be beneficial as well – especially with respect to enforcement processes.
AI-based tools in the areas of corporate governance, competition and anti-corruption are most commonly applied by supervisory and enforcement authorities to i) enhance their detection capabilities, and ii) increase the efficiency of enforcement actions. These two purposes are not mutually exclusive and should be viewed as intertwined. While the first focuses on enabling the detection of new forms of market manipulation and anti-competitive conduct that analog tools may not be able to detect, the second focuses on efficiency gains enabled by digital technologies in pre-existing enforcement processes.
The Colombia Superintendence of Industry and Commerce provides a good example of the use of an AI-supported price-monitoring tool to enhance detection of anti-competitive behaviour under its project “Sabueso”. It collects data on products sold on-line to help its investigators discover suspicious e-commerce pricing behaviour. The tool relies on machine learning to identify the same product in different on-line stores sold under different names and descriptions. The UK Serious Fraud Office provides an example of how AI is being used to strengthen the efficiency of enforcement actions. Scanning 600,000 documents a day, its use of AI in a criminal case in the UK reduced the pool of legally professional privileged documents needing review by an independent counsel by 80%. Beyond saving resources by reducing the timeline of the review process from two years to a few months, the use of AI also resulted in a more accurate and consistent review of the evidence.
SupTech tools can also help authorities improve their data collection and management capabilities, which can in turn improve data quality – itself a pre-requisite for enhanced data analysis (see Figure 1). For example, the Malaysia Securities Commission (SC Malaysia) uses AI to monitor and analyse the adoption of corporate governance best practices and quality of disclosures by listed companies. By requiring disclosure using a prescribed template that facilitates data extraction, the AI system supports evaluation and analysis which considers the type of information disclosed, depth of explanation and, in relation to departures, the strength of alternative practices.
However, adopting SupTech solutions also comes with challenges and risks, which apply both to AI-based tools and the wider set of SupTech solutions enabled by digital technologies more generally.
The main issues and constraints revolve around data quality, resourcing, and skills. Practical and legal challenges can also arise upon the integration of SupTech tools into legacy systems, while insufficient communication across all stakeholders involved may also hinder the effective implementation of AI-based solutions. Other notable technical issues and risks include risks related to digital security; third party dependencies; data localisation (potentially resulting in cross-border issues), as well as poor-quality algorithms, and opacity in the design and outputs of AI-based solutions (i.e. a “black box issue” potentially entailing reputational risks).
Overall, it is important that strategies for using technology – including AI – for supervision purposes be devised in consideration of authorities’ needs, regulatory frameworks and technological capacities. Although there is no “one-size-fits-all” approach, the chapter identifies important considerations that underpin successful SupTech strategies, ranging from the design to the implementation stage, and covering leadership, budget and skills concerns.
As a final word, greater co-ordination and collaboration between authorities, regulated entities and technology service providers within and across jurisdictions has an important role to play in improving the effectiveness of market regulators and public enforcement authorities. This contributes to: 1) ensuring the compatibility of innovative systems adopted by regulators and regulated entities; 2) fostering peer learning with respect to the successes and failures of SupTech uses; and 3) devising common standards and taxonomies for relevant regulatory areas to ensure the scalability and interoperability of SupTech tools. International organisations and standard-setting bodies can play an important role in that respect by convening and fostering exchanges among a wide range of stakeholders.
A chapter in the 2021 edition of the OECD Business and Finance Outlook examines how market regulators and public enforcement authorities are turning to supervisory technology (SupTech) tools and solutions as a means to improve their surveillance, analytical and enforcement capabilities, with a view to enhancing financial stability, market integrity and consumer welfare. The chapter first discusses the main drivers of SupTech, and it then describes the main benefits, challenges and risks.