Weekend Reading: Successful Bank Mergers Start with Effective Governance and Culture Metrics

By: Erich Hoefer , Co-Founder & COO of Starling

This piece first appeared in Starling Insights' newsletter on April 19, 2024. If you are interested in receiving our thrice-weekly newsletter, among many other benefits, please consider signing up as a Member of Starling Insights.

Of the many ways that organizational governance can be tested, mergers offer unique challenges. As two organizations come together, their distinct governance models often clash, leading to potential conflicts in decision-making processes, priorities, and strategic objectives.

The challenge for governance in such a scenario lies in balancing the maintenance of control and oversight while fostering an environment conducive to the seamless blending of systems and controls, practices, and cultures. Effective governance in this context therefore requires a meticulous approach to change management, clear communication channels, and a reevaluation of governance frameworks.

The US Office of the Comptroller of the Currency (OCC) is required by law to review combinations involving national banks and federal savings associations and, earlier this year, the agency proposed changes to its rules for evaluating bank mergers. One of the key motivations behind the proposed rules is to provide clarity to bank executives on the standards by which bank mergers will be assessed and to indicate which proposed combinations will trigger additional scrutiny.

Earlier this week, we at Starling Insights submitted our comments on that proposed rulemaking. You can find our complete comments on the OCC’s proposal here.

With respect to financial, capital, and liquidity risks, there exist well-established metrics that, at a minimum, allow executives and supervisors alike to identify and address potential problems. In addition to these well-established financial metrics, the OCC also evaluates mergers for what it refers to as Managerial Resources, which aim to capture less tangible matters like the governance capabilities of the acquirer, the target, and that of the ultimate combined entity.

In conducting this latter assessment, regulators like the OCC rely on such metrics as the number of outstanding matters requiring attention (MRA), current management ratings under the UFIRS system (also referred to as a CAMELS rating), as well as other standard supervisory methodologies. This, however, is where the OCC’s proposed rules fall short of providing the desired levels of clarity.

Take, for example, the case of Silicon Valley Bank (SVB), which failed in March 2023 after depositors withdrew over $140 billion in deposits over a series of days. One of the many post-mortems following this failure, the Review of the Federal Reserve’s Supervision of Silicon Valley Bank in April 2023 revealed that the supervisory team was planning to downgrade SVB’s CAMELS score but only in recognition of interest rate risk and mere months before that risk became essentially unmanageable. Aside from these red flags, SVB was considered by its supervisors to be a well-managed firm. According to the report, this view persisted even as it became, or should have become, increasingly apparent that management was not managing risk effectively.

If current supervisory tools fall short in accurately assessing management capabilities, then the guidance being offered to banks by the OCC may not provide the degree of clarity being promised. Leaders of acquiring banks may pursue business combinations, confident in their compliance with these rules, only to later face scrutiny because their prevailing management capability ratings did not accurately reflect the true safety and soundness of the combined entity (or its predecessors).

This observation is not intended as a criticism of the supervisory teams that evaluated SVB but, rather, it is to emphasize the difficulty in evaluating governance absent effective measurement tools. Unlike financial risks, which yield to quantitative analysis, the assessment of management resources and capabilities depends on developing a reliable understanding of the ways in which intangible governance structures operate to produce performance outcomes.

These include things like firm culture: employee behavioral norms, understood if unwritten expected practices, invisible but consequential networks of internal peer influence, and generalized operational norms that explain how work actually gets done and which reflect the practical, day-to-day lived realities among personnel — “the way things are done around here.”

Such factors are material contributors to risk and, in many cases, they represent the most critical challenges to risk governance. But because we have poor means of identifying, measuring, and managing these elements, they are habitually neglected — by firms and their supervisors.

Through our past reporting in the Compendium and elsewhere, we have noted how a number of supervisory bodies are encouraging innovative approaches that seek to add more structure and rigor to culture supervision. Efforts are underway to implement effective culture and behavior assessments, to design more impactful executive accountability regimes, to improve root cause analysis, and to embed effective behaviors into first-line operations.

Key to the success of these efforts is the development of reliable metrics by which firms can assess themselves on culture and behavioral risk indicators and compare themselves vis-à-vis peers on a horizontal-review basis. By making it possible to process vast troves of internal corporate data at scale, machine learning holds promise in this connection.

For example, we can now generate “predictive behavioral analytics” — continuously updated behavioral indicators that provide an accurate, real-time view of the state of a firm’s informal governance structure in vivo. Such predictive behavioral analytics enable a new generation of governance and supervisory tools that permit for more quantitative approaches to assessing organizational governance.

Given that, we should anticipate a time when executives contemplating a strategic combination or a supervisory team providing oversight can look to a common set of metrics that demonstrate both the acquirer and target have implemented effective governance solutions. With business combinations, clarity and speed around regulatory decisions is of critical importance. By embracing the use of AI and advanced analytics, firms will be better positioned to mitigate problem areas and to respond more quickly to regulatory concerns.