Artificial intelligence and machine learning in financial services
This report considers the financial stability implications of the growing use of artificial intelligence (AI) and machine learning in financial services.
Financial institutions are increasingly using AI and machine learning in a range of applications across the financial system including to assess credit quality, to price and market insurance contracts and to automate client interaction. Institutions are optimising scarce capital with AI and machine learning techniques, as well as back-testing models and analysing the market impact of trading large positions. Meanwhile, hedge funds, broker-dealers and other firms are using it to find signals for higher uncorrelated returns and to optimise trade execution. Both public and private sector institutions may use these technologies for regulatory compliance, surveillance, data quality assessment and fraud detection.
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 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, for instance based on the use by various institutions of previously unrelated data sources.
Network effects and scalability of new technologies may 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.