Supervisory Implications of Artificial Intelligence and Machine Learning

Financial institutions across all sectors are increasingly making use of artificial intelligence (AI) and machine learning (ML). Examples - from both developed and emerging economies - include the credit scoring of loan applications, “chatbots” for communication with customers, and identifying suspicious financial transactions. The use of AI and ML can bring many benefits and opportunities, including expanded access to financial products and services. But their use may also change the nature of familiar risks (such as credit and insurance underwriting risks); generate new risks (the “black box” nature of ML applications); and give rise to new considerations around the ethical use of AI and ML (to avoid unfair bias and discrimination). It is therefore important for supervisors to understand the risks to the safety and soundness of financial institutions, financial stability, and consumer protection.

This Note describes some of the uses of AI and ML by financial institutions; considers the supervisory responses to such uses; and highlights some ways in which supervisory authorities can themselves use AI and ML (a subset of “Suptech”) to improve the effectiveness and efficiency of supervision.

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