Deep Learning and Financial Stability
45 Pages Posted: 13 Nov 2020
Date Written: November 1, 2020
The financial sector is entering a new era of rapidly advancing data analytics as deep learning models are adopted into its technology stack. A subset of Artificial Intelligence, deep learning represents a fundamental discontinuity from prior analytical techniques, providing previously unseen predictive powers enabling significant opportunities for efficiency, financial inclusion, and risk mitigation. Broad adoption of deep learning, though, may over time increase uniformity, interconnectedness, and regulatory gaps. This paper maps deep learning’s key characteristics across five possible transmission pathways exploring how, as it moves to a mature stage of broad adoption, it may lead to financial system fragility and economy-wide risks. Existing financial sector regulatory regimes - built in an earlier era of data analytics technology - are likely to fall short in addressing the systemic risks posed by broad adoption of deep learning in finance. The authors close by considering policy tools that might mitigate these systemic risks.
Keywords: Deep Learning, Neural Networks, Artificial Intelligence, Financial Stability, Systemic Risk
JEL Classification: G00, G01, G18, G32, G38, C00, C45
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