The BIS Innovation Hub Singapore Centre and the Monetary Authority of Singapore (MAS) have developed a new prototype platform integrating regulatory data and analytics. Known as Project Ellipse, the platform 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.
The BIS will launch a collaboration community to share, test, customize and scale this solution across regulatory authorities worldwide. The Ellipse prototype will be published on BIS Open Tech, a new platform for sharing statistical and financial software as public goods, promoting international cooperation and coordination.
“Regulators need accurate and timely information to assess emerging risks and to make informed supervisory decisions,” said Ross Leckow, acting head of the BIS Innovation Hub. “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. It can 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.”
“Recent technological advancements have opened up possibilities for supervisors to leverage more granular, timely and varied datasets to significantly improve supervisory effectiveness,” added Hern Shin Ho, deputy managing director (financial supervision) for the MAS. “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 furthering joint initiatives to develop common suptech solutions for supervisors”,
With the support of the Bank of England, the International Swaps and Derivatives Association, Accenture and Financial Network Analytics, the project was undertaken in two phases:
Phase one investigated how machine-executable digital reporting could enable data-driven supervision using a cross-border standard 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 identifying risk correlations and sentiment analysis to alert supervisors in real-time to issues that may need further investigation.