The Financial Stability Board (FSB) has published a report that considers the financial stability implications of the growing use of artificial intelligence (AI) and machine learning in financial services.
The FSB’s analysis reveals a number of potential benefits and risks for financial stability that should be monitored as the technology is adopted in the coming years and as more data becomes available. They are:
- the more efficient processing of information, for example in credit decisions, financial markets, insurance contracts, and customer interaction, may contribute to a more efficient financial system. The applications of AI and machine learning can help improve regulatory compliance and increase supervisory effectiveness;
- at the same time, 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;
- applications of AI and machine learning could result in 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;
- the lack of interpretability or “auditability” of AI and machine learning methods could become a macro-level risk. Similarly, a widespread use of opaque models may result in unintended consequences; and
- as with any new product or service, there are important issues around appropriate risk management and oversight. 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 are important to ensure applications do what they are intended to do.
Overall, the FSB finds that AI and machine learning applications show substantial promise if their specific risks are properly managed.