FSB published a report examining the financial stability implications of the growing use of artificial intelligence (AI) and machine learning in financial services. The report draws on discussions with firms; academic research; public and private sector reports; and ongoing work at FSB member institutions.
The first section of the report defines the key concepts of the report and offers context for the development of AI and machine learning for financial applications. The section describes supply and demand factors driving the adoption of these techniques in financial services. The third section describes four sets of use cases: customer-focused applications; operations-focused uses; trading and portfolio management; and regulatory compliance and supervision. The fourth section contains a micro-analysis of the effects of adoption on financial markets, institutions, and consumers. The fifth section offers a macro-analysis of effects on the financial system, with the final section concluding with an assessment of implications for financial stability. The FSB analysis reveals that the following potential benefits and risks for financial stability should be monitored, as the technology is adopted in the coming years and as more data becomes available:
- The more efficient processing of information—for example in credit decisions, financial markets, insurance contracts and customer interactions—may contribute to a more efficient financial system. The applications of AI and machine learning by regulators and supervisors can help improve regulatory compliance and increase supervisory effectiveness.
- Applications of AI and machine learning could result in new and unexpected forms of interconnectedness between financial markets and institutions, based on the use of previously unrelated data sources by various institutions.
- 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.
- 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.
- 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.
Related Link: FSB Report (PDF)
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