I believe this simple framework is useful when assessing and discussing changes to supervisory or regulatory policy that can be designed with different purposes in mind. For example, a policy change could be designed to increase efficiency and move toward the frontier. This could entail changing rules, regulations, or supervisory practices in order to promote more financial intermediation without increasing risk. Obvious examples could be eliminating a complex compliance regime for an activity that banks no longer perform or reducing redundancy in data reporting. It seems like a good idea to always pursue these types of efficiency gains. 1/7  BIS central bankers' speeches  Alternatively, a policy change could be designed to achieve a different risk and return profile. This could be a shift from a mix of policies designed to achieve a low-intermediation and low-risk outcome to a mix of policies designed to support higher intermediation while accepting greater risk. Either of these regimes can be efficient and consistent with policymakers’ preferences, but the expected outcomes would be quite different. From a public policy perspective, in my view, it is important to be clear and distinguish between these two types of policy change. For example, one might expect a greater number of bank failures during stress if there were less effective supervisory oversight or higher borrowing costs in a world with higher capital requirements.4 Those may not be desired outcomes, but they would be predictable ones. From a risk management perspective, this might be viewed as an accepted risk of a particular policy choice.

For example, the Basel Committee published a framework to identify optimal levels of bank capital and used that framework as an input into the calibration of Basel III capital requirements.10 This approach considered how the probability of a financial crisis varies with bank capital, the social costs of banking crisis, the link between bank capital and bank funding costs, and the impact of higher loan spreads on economic activity. Moreover, this efficient frontier is not fixed and is likely to vary over time as technology, official sector tools, and bank business models evolve. For example, technology-driven innovations that rely on bigger datasets and more sophisticated predictive algorithms might change the risk/return opportunities associated with certain intermediation services. Similarly, supervisory innovations like stress testing or more sophisticated horizontal assessments could make official sector oversight more effective. Finally, changes in business strategies like a shift to a more diversified business model might change the overall risk of certain activities. One can think of these changes as shifting the efficient policy frontier where there is more intermediation for a given level of risk. Alternatively, constraints on supervisory authorities or practices could shift the frontier in the opposite direction if use of effective tools is prohibited. Given the complexity of these relationships, it is not surprising that there is a wide range of estimates and theories about the optimal type and level of oversight of financial institutions. Nonetheless, we need to understand better these relationships in order to make policy choices more effective.