Deep Learning and Financial Regulation

Coker, D. 2020. Deep Learning and Financial Regulation. in: Chishti, S., Bartoletti, I., Leslie, A. and Millie, S.M. (ed.) Wiley. pp. 251-253

Chapter titleDeep Learning and Financial Regulation
AuthorsCoker, D.
EditorsChishti, S., Bartoletti, I., Leslie, A. and Millie, S.M.
Abstract

Financial regulators are responsible for the public good. As the state is considered the “lender of last resort”, failure of a regulated financial institution may result in costs being paid by the public. As financial innovation accelerates the established cycle of crisis, bailout and reactively increasing regulation won't work. Autonomous regulatory agents (ARAs) will change the regulatory paradigm and break this cycle. Driven by deep learning, the complex regulatory web will be reduced to first principles reflecting what constitutes the common good. A regulatory framework mediated by ARAs, with decisions driven by deep learning, will not create contradictions in the first instance. Over time, ARAs trained by financial regulators will gain significant insights. ARAs will also be used by financial institutions to ensure regulatory compliance. ARAs must be capable of fully explaining their decisions.

Page range251-253
Year2020
PublisherWiley
Publication dates
Published13 May 2020
ISBN9781119551904
9781119551966
Digital Object Identifier (DOI)https://doi.org/10.1002/9781119551966.ch68
Web address (URL)http://dx.doi.org/10.1002/9781119551966.ch68

Related outputs

Why El Salvador has adopted Bitcoin as legal tender
Coker, D. 2021. Why El Salvador has adopted Bitcoin as legal tender. Evening Standard.

Post-crisis, US banks have recovered while their European peers are still looking for ways to survive
Coker, D. 2017. Post-crisis, US banks have recovered while their European peers are still looking for ways to survive. CNBC.

Permalink - https://westminsterresearch.westminster.ac.uk/item/v93v8/deep-learning-and-financial-regulation


Share this

Usage statistics

61 total views
0 total downloads
These values cover views and downloads from WestminsterResearch and are for the period from September 2nd 2018, when this repository was created.