The Monetary Authority of Singapore (MAS) and the Innovation Hub Singapore Centre under a Swiss-based firm have developed a new prototype platform that integrates regulatory data and analytics. Known as Project Ellipse, the platform successfully demonstrates how regulatory and other data, such as articles and news, can be integrated into a single platform to help regulatory authorities identify potential risks to individual banks and the banking system.
To enable collaboration, the Hub will launch an Ellipse collaboration community to share, further test, customise and scale this solution across regulatory authorities around the world. The Ellipse prototype is the first to be published on BIS Open Tech, a new platform for sharing statistical and financial software as public goods, thereby promoting international cooperation and coordination.
The Acting Head of the Innovation Hub stated that regulators need accurate and timely information to assess emerging risks and to make informed supervisory decisions. Project Ellipse has now developed a potential tool for the global regulatory community to further explore and collaborate on common solutions that can improve the data and analytical capabilities of regulatory authorities.
He added that this has the potential to be a game-changer by giving supervisors access to more and better data, structured and unstructured, with greater predictive insights than ever before, it can be scaled to provide real-time analysis on a national or cross border supervisory basis.
Meanwhile, the Deputy Managing Director (Financial Supervision), MAS noted that recent technological advancements have opened up possibilities for supervisors to leverage more granular, timely and varied datasets to significantly improve supervisory effectiveness.
Project Ellipse demonstrates that the collection and use of such datasets need not be prohibitive, but can be codified, efficient, cost-effective and potentially scalable even on a cross border basis. MAS is adapting the prototype for our own supervisory needs. I hope other supervisors will similarly find it useful and look forward to further joint initiatives to develop common SupTech solutions for supervisors.
The project was undertaken in two phases:
- Phase one of the project investigated how machine-executable digital reporting could enable data-driven supervision, using a cross-border common data model.
- The second phase examined how advanced analytics such as machine learning and natural language processing could be applied to unstructured and granular reporting data. This allows the identification of risk correlations and sentiment analysis, to alert supervisors in real-time to issues that may need further investigation.
The World Bank notes that across the globe, financial sector supervisors are seeing a profound shift to data-driven supervision enabled by the latest in technology and data solutions.
While technology and data are not new to financial oversight, their specific application to financial consumer protection and market conduct supervision has become more widespread and sophisticated in recent years. Expanding on the World Bank’s 2018 note on supervisory technology, or suptech, states that increasing operational efficiency and enhancing supervisory effectiveness are two of the primary motivations for adopting suptech solutions for market conduct.
Two different motivations often drive financial authorities in their implementation of suptech. Namely:
- increasing operational efficiency and,
- improving hypothesis-driven supervision.
The report also notes that the growth in digital activity provoked by the COVID-19 pandemic re-emphasizes the necessity and value of suptech for financial authorities. This is true for all categories of suptech solutions for market conduct.
The direct and automated collection of granular regulatory data from supervised institutions is critical to replacing on-site examinations, as is the ability of supervisors to engage directly with consumers and manage their complaints with providers digitally. Meanwhile, both non-traditional market monitoring and advanced text analysis allow supervisors to monitor fast-moving sentiment remotely and emerging risks to consumers on a more rapid basis.
