The first thing to note is that some had indicated a wrong application of an analytical algorithm as part of the reasons behind the 2008 market collapse, in its aftermath: the risk of automated analysis in stock markets is far from new.
So, a statement like «A.I. could be the next big systemic risk to the financial system» (in the NYT newsletter page that triggered the Axios summary) looks strongly anachronistic.
The actual point seems to be about the replacement of transparent algorithms with black-boxes, as one may understand from Gary Gensler's article in 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
So, a statement like «A.I. could be the next big systemic risk to the financial system» (in the NYT newsletter page that triggered the Axios summary) looks strongly anachronistic.
The actual point seems to be about the replacement of transparent algorithms with black-boxes, as one may understand from Gary Gensler's article in 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
at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3723132&...