Community banks continue to face strong headwinds entering 2014 from regulatory and competitive pressures that have hobbled this sector for years. As large banks have engaged in significant restructuring and investment in technology since the crisis, community banks' lack of scale and limited resources significantly hamper their ability to effectively leverage technology and data that would help them serve customers and manage risks. Some capabilities, such as data warehousing and technical risk analytics, could be significantly strengthened via shared services arrangements.
A sign that community banks are losing the battle against large banking institutions is the continued widening of the gap between operating efficiency ratios of community and large banks. Operating efficiency is defined as the ratio of noninterest expenses to net operating revenues, where lower ratios signify greater efficiency. For the last three quarters of 2013, the efficiency ratios for banks with less than $100 million and $100 million to $1billion in assets was about 77% and 70%, respectively. Contrast that with efficiency ratios of banks of $1 billion to $10 billion (64%) and greater than $10 billion in assets (59%) and small banks clearly are underperforming large institutions.
More troubling is that the gap in efficiency ratios between community and other banks since 1998 has increased and dramatically widened since the financial crisis, as revealed in a 2012 study by the FDIC. The FDIC attributed the majority of the increase in community bank (defined by FDIC by a number of attributes, including asset size) efficiency ratios to deterioration in net income as well as, to a lesser extent, an increase in noninterest expenses since 2009.
While regulators have been mindful to some degree of potential regulatory burden and the costs it imposes on community banks since the crisis, the sector has still experienced significant regulatory and compliance focus from safety and soundness regulators and the Consumer Financial Protection Bureau. Capacity issues preclude small institutions from making the kind of investments in technology and analytics that can put these banks in a better competitive position. Investment in data warehouses, data processing infrastructure and tools to analyze the data allows banks of any sizeáto create better products for their customers, while mitigating compliance and other risks to the firm. However, such infrastructure takes scale to put in place and that's where a shared services arrangement might improve the condition of many community banks.
Imagine creating a data and analytics utility for a consortium of community banks where each bank has an ownership stake in the utility. Institutions would put up capital to support the utility's activities, which are solely to acquire, manage, and analyze data provided by each bank. The utility would effectively take on the structure of a cooperative and in addition to financial support each member bank would provide its transaction-level data to the utility on a regular basis. The utility would be managed by a small independent team overseen by a board made up of representatives from the member banks. Each institution would receive back from the utility each month a detailed report on its performance based on its own data. The utility could also be used to compile regulatory reports and data submissions or even assist in developing the loan loss reserve, among other important and staff-intensive exercises (overseen and approved, of course, by bank management). While no proprietary information on individual banks would be released by the utility to other member banks, the monthly reports would provide peer benchmarks created from the collective membership data. Ad hoc analysis would also be a service provided to each bank according to need and limited by service-level agreements between the utility and members.
Such a cooperative could significantly improve the fortunes of community banks by reducing costs of highly specialized and expensive services, while allowing these firms to enjoy a level of data and analytical performance generally available to only large banks. Such capabilities have enormous potential to improve community banks' abilities to manage traditional risks such as credit, liquidity and interest rate risk as well as create a rich source of data for measuring and monitoring operational and compliance risk. Equally important, insights on product performance could vastly improve a bank's product offerings and strategic direction. In addition, this model could provide a compelling alternative to a more formal and unwanted way of leveraging common technology, namely through a merger or acquisition. Assuming a well-structured utility model can be created, regulators should welcome such enhancements and encourage their development with proper governance and controls.
Community banks must continue to think creatively about how they can maintain their independence over the long run. Banking is increasingly a data-driven business and while retaining the benefits of a customer-facing model is an essential ingredient to community banking's success, it must be augmented with capabilities that allow the sector to make improvements by insights gained from robust data and analytic services.
Clifford Rossi is the Professor-of-the-Practice at the Robert H. Smith School of Business at the University of Maryland.