Person:

Scott, Hal

This article explores the importance of an efficient retail payment system and develops an integrated framework for evaluation of the retail payment system by policy makers. It examines the costs and benefits of the various types of retail payment system, focusing on the seven desirable benefits of the retail payment system: (1) finality and reversibility; (2) universality (ability to use at point of sale and remotely); (3) recordkeeping; (4) liquidity (maximizing interest earning assets); (5) security and safety; (6) financial inclusion and access; and (7) fungibility and ease of use (seven benefits).

The article discusses the Coase Theorem, a proposition from transaction cost economics that provides a useful tool for analyzing transaction efficiency. Increased costs are not bad per se since parties are often willing to incur higher costs to achieve their desired results, e.g. higher costs for a more secure form of payment. Indeed, higher costs may often generate higher value to both parties to a transaction. What one wants to reduce are “friction” costs, costs that neither party wants to pay to achieve a desired result, e.g. higher costs produced by lack of information.

While each retail payment system provides certain advantages, e.g. cash for small transactions, overall the analysis suggests that debit and credit cards represent the most desirable payment system for achieving the seven benefits set forth above. This is supported by statistics that indicate that retail payments have increasingly moved toward card payments.

Operational risk is fundamentally different from all other risks taken on by a bank. It is embedded in every activity and product of an institution, and in contrast to the conventional financial risks (e.g. market, credit) is harder to measure and model, and not straight forwardly eliminated through simple adjustments like selling off a position. While it varies considerably, operational risk tends to represent about 10-30% of the total risk pie, and has grown rapidly since the 2008-09 crisis. It tends to be more fat-tailed than other risks, and the data are poorer. As a result, models are fragile – small changes in the data have dramatic impacts on modeled output – and thus required operational risk capital is unstable. Yet the U.S. regulatory capital regime, the central focus of this paper, is surprisingly more rigidly model-focused for this risk than for any other. We are especially concerned with the absence of incentives to invest in and improve business control processes through the granting of regulatory capital relief. We make three, not mutually exclusive policy suggestions. First, address model fragility directly through regulatory anchoring of key model parameters, yet allow each bank to scale capital to their data using robust methodologies. Second, relax the current tight linkage between statistical model output and required regulatory capital, incentivizing prudent risk management through joint use of scenarios and control factors in addition to data-based statistical models in setting regulatory capital. Third, provide allowance for real risk transfer through an insurance credit to capital, encouraging more effective risk sharing through future product innovation. Until our understanding of operational risks increases, required regulatory capital should be based on methodologies that are simpler, more standardized, more stable and more robust.