Document Type

Article

Publication Date

February 2020

First Page

1

Last Page

55

Abstract

The processing of retail payments has traditionally been the domain of regulated banks, but technologically sophisticated players like Venmo, AliPay, Bitcoin and Ripple (and potentially Facebook’s Libra) are making incursions into the market. Even within regulated banks, payments processing is becoming increasingly reliant on new technologies – JPMorgan Chase’s “JPMCoin” is just one example. However, limited attention has been paid to the new kinds of operational risks associated with these complex new methods of processing retail payments. This Article argues that technological failures at a payments provider (bank or non-bank) could be amplified in unexpected ways as they interact with technological failures at other payments providers. In a worst case scenario, a cascading failure of payments technologies could cause significant parts of the retail payments system to shut down – an eventuality that would have a significant economic impact if people were unable to transact for a prolonged period of time.This Article is the first to raise the possibility of a financial crisis precipitated primarily by operational failures – such a crisis would look more like a rolling blackout than a bank run. Because of this possibility, this Article argues that it is insufficient to approach the risk of payments failure with a purely prudential strategy. This Article therefore makes the case for a complementary “macro-operational” approach to regulation, rooted in complexity theory, to deal with the possibility that our retail payments system – and the broader economy – could be hobbled by the systemic interactions of operational risks. Using this framework, this Article begins to analyze the potential threats posed by different technologies and business models to the orderly functioning of our retail payments system, and also suggests the beginnings of what proactive macro-operational regulation of the retail payments system might look like.

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