Deep Learning and Financial Stability

45 Pages Posted: 13 Nov 2020

See all articles by Gary Gensler

Gary Gensler

Masachusetts Institute of Technology (MIT) Sloan School of Management

Lily Bailey

Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science

Date Written: November 1, 2020

Abstract

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

Suggested Citation

Gensler, Gary and Bailey, Lily, Deep Learning and Financial Stability (November 1, 2020). Available at SSRN: https://ssrn.com/abstract=3723132 or http://dx.doi.org/10.2139/ssrn.3723132

Gary Gensler (Contact Author)

Masachusetts Institute of Technology (MIT) Sloan School of Management ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Lily Bailey

Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science ( email )

77 Massachusetts Avenue
Cambridge, MA 02139-4307
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
3,161
Abstract Views
7,363
rank
4,262
PlumX Metrics