Supervisory bank risk early warning modeling: an examiner’s first line of defense

Christopher C. Henderson; Shaohui Jia; Charles Mattioli

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This paper synthesizes the early warning system literature and highlights the unique problem federal regulators have in optimizing their supervisory objectives subject to binding legal constraints that have changed significantly over a short period of time (eg, regulation based on the Dodd-Frank Act of 2010 and regulation based on the Economic Growth, Regulatory Relief, and Consumer Protection Act of 2018).
This paper formalizes a class of models that can properly align prudential regulatory goals to the difficulty in monitoring inherent and emerging banking risks at community banks.
This paper leverages reasonable regulatory models that broadly capture the information set of examination staff when conducting bank exams and statistically evaluates the predictive accuracy of these models using robust econometric tests with appropriate asymptotic properties.
The paper results show that supervisory early warning models can be effective tools to identify inherent and emerging risks. These models are important to achieve regulatory goals of promoting safe and sound banking by efficiently identifying poor bank performance that could lead to financial instability.

Abstract

The protracted period of stability in the banking sector since the Great Recession, accompanied by the evolving time path of interest rates, makes understanding the causes and timing of the next economic downturn particularly acute for regulatory agencies. The development and implementation of supervisory ratings models is critical in providing a first response by regulatory agencies to shift examination resources to those institutions that pose the greatest risk to bank solvency or financial stability. In alignment with the Economic Growth, Regulatory Relief, and Consumer Protection Act of 2018, examiners could enhance prudential regulation standards through data-enhanced activities to monitor inherent and emerging risk, especially at small depository institutions and holding companies where reduced reporting requirements and extended examination cycles have been implemented under the Act. The results of this paper show that robust forward-looking statistical models are superior to backward-looking assessments of supervisory compliance, which could lead to less regulatory burden when integrated into the examination process, particularly at smaller institutions.

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