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Markets

Machine learning, AI pose host of potential risks, benefits in financial services — FSB

The rise of machine learning and artificial intelligence in financial services may pose risks to financial stability as well as benefits, warns the Financial Stability Board.

The FSB said in a report considering the financial stability implications of greater use of AI and machine learning that financial institutions are increasingly using the tools to assess credit quality, to price and market insurance contracts and to automate client interactions. "Institutions are optimizing scarce capital with AI and machine learning techniques, as well as back-testing models and analyzing the market impact of trading large positions."

On the potential benefits side, greater application by regulators and supervisors can help improve regulatory compliance and increase supervisory effectiveness.

Also on the positive side, applications of AI and machine learning could lead to "new and unexpected forms of interconnectedness between financial markets and institutions, for instance, based on the use by various institutions of previously unrelated data sources," said the report.

On the risk side, network effects and scalability of new technologies may, in the future, "give rise to third-party dependencies. This could in turn lead to the emergence of new systemically important players that could fall outside the regulatory perimeter."

A further macro-level risk could be realized in the lack of interpretability or auditability of AI and machine learning methods, it said.

The FSB added: "As with any new product or service, it will be important to assess uses of AI and machine learning in view of their risks, including adherence to relevant protocols on data privacy, conduct risks and cybersecurity. Adequate testing and 'training' of tools with unbiased data and feedback mechanisms is important to ensure applications do what they are intended to do."

The report also outlines a number of potential market vulnerabilities. While applications by hedge funds, for example, to find new and uncorrelated trading strategies could result in greater diversity in market movements, on the other hand, new trading algorithms based on machine learning "may be less predictable than current rule-based applications and may interact in unexpected ways. To the extent that firms using AI or machine learning techniques can generate higher returns or lower trading costs, it is likely that incentives for adoption will increase. In the absence of data on the extent of marketwide use, market movements may be ascribed to AI and machine learning models, and interpretation of market shocks may be hampered," said the report.

A further potential source of vulnerability is the use of AI and machine learning in high-frequency trading, the FSB said.