May 15, 2019

During the opening address at the Asia-Pacific Risk Management Council Q2 Meeting, Dr. David Hardoon, the Chief Data Officer of MAS, remarked on whether fintech and digital innovations could provide an ultimate solution for risk management. He also briefly elaborated on the three key risks—cyber-security, data privacy and protection, and unfair discrimination while using artificial intelligence—and the work being done at MAS to address these risks. He highlighted that MAS has employed greater use of data analytics for risk detection, has partnered with the industry to develop a set of principles to encourage responsible use of technologies, and has developed an Augmented Intelligence tool that automates the computation of key metrics for trade analysis.

Mr. Hardoon highlighted that cyber risk remains a key risk that MAS and financial institutions in Singapore are closely monitoring. Given the highly interconnected financial system, borderless nature, and increasing complexity of cyber-attacks, it takes a concerted effort and close collaboration among stakeholders in the ecosystem to manage the risks and maintain cyber resilience. Apart from the planned issuance of a new MAS Notice on cyber hygiene requirements, MAS has recently consulted on proposed revisions to the Technology Risk Management Guidelines and Business Continuity Management Guidelines, which will serve to help financial institutions better manage cyber risk. Besides these regulatory efforts, MAS has also been taking a collaborative approach by partnering the industry to conduct cyber exercises, share cyber threat intelligence, and establish industry standards and guidance to promote cyber resilience.

Regarding the risk of unfair discrimination, he added that increasing use of artificial intelligence) has given rise to the risk of “black boxes” in decision-making. Financial institutions are struggling to validate artificial-intelligence-based models that use continuous learning and adaptation as distinct from fixed parameters and historical back-testing. Regulators have started to detect cases where artificial-intelligence-based decision-making has led to systematic exclusion of certain demographics. When an artificial intelligence tool finds an empirical basis for discriminating by a combination of variables such as gender, ethnicity, religion, and nationality, say for a loan or insurance decision, the concern is how much of that empiricism is grounded in reality and how much of it is due to unobserved biases in society that the artificial intelligence is learning from. He suggested that encouraging safe, fair, and trustworthy innovation also means that ethical and responsible use of technology by every ecosystem player is key.

In the area of artificial intelligence and data analytics, MAS has partnered with the industry to develop a set of principles to encourage responsible use of these technologies. These are known as the Fairness, Ethics, Accountability and Transparency (FEAT) principles. As financial institutions increasingly adopt technology to support business strategies and in risk management, the FEAT principles are intended to provide guidance on internal governance around data management and use of these technologies. Earlier this year, the InfoComm Media Development Authority (IMDA) also released Singapore’s Model Artificial Intelligence Governance Framework. This Model Framework is the first in Asia to provide detailed and readily implementable guidance to private-sector organizations to address key ethical and governance issues when deploying artificial intelligence solutions. This is another set of best practices that can be considered. 

Finally, while discussing whether digital innovations have the potential to provide an ultimate solution for risk management, he described the work of MAS in the areas of artificial intelligence, data analytics, and risk detection. In the area of artificial intelligence and data analytics, the use of these technologies can assist in risk monitoring and management in various areas, such as anti-money laundering, fraud detection, internal compliance, and business or market risks. MAS has employed greater use of data analytics for risk detection and targeting, using suspicious transaction reports and other data sets. This has enabled MAS to identify suspicious fund flow networks more effectively and focus supervisory attention on networks of higher risk accounts, entities, or activities. MAS also developed Project Apollo, an Augmented Intelligence tool that automates the computation of key metrics for trade analysis and predicts the likelihood that an expert will opine that market manipulation has occurred. The use of this technology helps improve detection of market abuse. These are just a few examples of technology that the industry can also adopt to unlock insights—whether to sharpen the surveillance of risks or to transform the way work is done, opined Mr. Hardoon.

 

Keywords: Asia Pacific, Singapore, Banking, Securities, Regtech, Fintech, Artificial Intelligence, FEAT Principles, AI Governance Framework, MAS

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