How Technology Investment Drives Community Bank Consolidation

From 2011 to 2019, the number of bank charters fell by nearly one-third, from 7,357 to 5,177, with community banks accounting for three-quarters of this decline (FDIC Community Bank Report, 2020) In a new paper, we presents compelling evidence that the decline stems largely from bank mergers driven in part by a desire to create economies of scale through technology investments. The need for technology investment has been  propelled by regulatory requirements, consumer preference for digital services, and the pursuit of operational efficiencies, such as streamlined loan processing.

Nearly all community banks use technology services from Fiserv, FIS, and Jack Henry for their core banking needs (The Conference of State Banking Supervisors Annual Surveys in 2019, 2020, 2021, and 2022 report that under 1 percent of the respondents rely exclusively on in-house resources to support their technology). These core service providers (CSPs) handle back-end support for everyday transactions, updating accounts, and processing deposits, loans, and credits. Middleware layers add support for essential functions like loan and payment management, customer relationship management, regulatory reporting, and mobile banking (see here). Despite high ratings in security, technology, and risk management, community bankers find CSPs lacking in cost efficiency, contract flexibility, and innovation speed (Conference of State Banking Supervisors Annual Survey, 2019). While services from the three main CSPs are similar, transitioning between them or integrating third-party systems involves significant costs and challenges (Shevlin, 2021a).

In an ideal market where all community banks access the same technology, there’s no inherent advantage to investing in technology investment to create economies of scale. Yet, there are market frictions such as long-term contracts with CSPs that mix fixed and variable fees and penalize early termination during mergers (Shevlin, 2021b). Additionally, desired technology upgrades are often bundled with unwanted products (The source for this information is multiple informal conversations with community bank executives by one of the authors). Therefore, the fixed costs of tech investments can lead to scale economies (or diseconomies), depending on the bank’s ability to enhance customer revenue or operational efficiency. Banks poised for growth can achieve economies of scale and boost profitability through strategic tech investments, while those investing merely to maintain their customer base may struggle to do so.

The impact of technology investments on community bank mergers and acquisitions is ambiguous due to two conflicting effects. On one hand, the economies of scale effect suggests that banks with significant technology investments become attractive acquisition targets if these investments do not enhance productivity on their own, offering acquirers a chance to integrate these banks and extend their product offerings. This makes technology-rich community banks appealing for M&A activity. On the other hand, the high cost of integration suggests that banks with advanced technology may deter acquirers if the technology presents significant integration challenges or compatibility issues, particularly when the CSPs differ between the target and acquiring banks. Those  costs could outweigh the benefits, potentially reducing the likelihood of technology-heavy banks being acquired.

To analyze the effects of technology investments on community banks, we use the data processing cost (DPC) from annual call reports, scaled by total assets, as an indicator. Unlike larger banks, community banks’ DPC, a significant noninterest, reflects fees paid to CSPs. From 2010 to 2019, DPC for banks with under $10 billion in assets grew by 4.9 percent annually, outpacing the -0.62 percent and 2.18 percent growth rates for other noninterest and total noninterest expenses, respectively. Despite typically being categorized as a capital expense subject to depreciation, the reliance of nearly all community banks on CSPs translates these technology investments directly into operational data processing expenses.

Our empirical analysis employs logistic regressions to examine how technology investments influence the probability of community banks becoming targets or acquirers in M&A transactions. We categorize community banks as those with assets under $10 billion, with a robustness check for banks under $1 billion. Our analysis accounts for various bank characteristics – size, profitability, equity capitalization, riskiness, efficiency, and ownership type – that could affect both technology investment decisions and M&A activity. Additionally, we adjust for local demographic and economic conditions and include year, state, and bank category fixed effects to ensure comprehensive control.

Our analysis from the 2010s demonstrates that community banks with greater technology investments had a higher likelihood of being acquisition targets, supporting the economies of scale argument. Specifically, each 1 percent increase in technology investments raised the chance of being acquired the next year by 0.2 percent, a significant impact given the baseline acquisition probability of 3.6 percent. A bank’s technology investment did not influence its likelihood of being an acquirer. Targeted banks were typically smaller, less profitable, and posed lower risks than those not involved in mergers. Furthermore, our research underlines the importance of market structure, showing a positive correlation between a bank’s merger target probability and the population and income of the county, suggesting that acquirers from smaller markets aim to enter larger, more promising markets.

To address potential endogeneity in our analysis of community banks’ technology investments and their acquisition likelihood, we implement two strategies: coarsened exact matching and an instrumental variable approach. Using coarsened exact matching, we categorize banks into treatment and control groups based on technology investments, matching them on state, year, and bank category, along with coarsening other characteristics for precise one-to-one comparison. This refined analysis confirms that higher technology investments increase a bank’s acquisition likelihood. Additionally, we employ an instrumental variable approach, using the local senior population proportion and broadband internet availability in each county as instruments. These factors indirectly affect technology investments without directly influencing M&A activity, controlling for demographic and economic conditions. These technology investments significantly raise a community bank’s chance of being targeted for acquisition.

To evaluate the effect of mergers on acquirer banks’ performance, we adopt a treatment effect methodology following Cattaneo (2010), treating the merger as the treatment. Through propensity score matching, we identify a comparable non-M&A-involved bank for each acquirer bank. Our analysis reveals that acquirers’ operational efficiency improves in the second and third years after a merger, supporting the economies of scale hypothesis. This suggests that acquirer banks initially spend time integrating the target’s technology investments, achieving operational efficiencies and economies of scale in subsequent years.

We also perform various robustness checks, including alternative definitions of community banks, different technology investment measures, estimation models, and a sample limited to banks with at least some technology investments. These checks consistently reinforce our main finding: Technology investments increase the likelihood of community banks being acquired.

Our study contributes to the literature in three significant ways. First, it provides statistical evidence of the critical role of technology investments in the consolidation of community banks, illustrating how such investments influence their acquisition likelihood. This aspect introduces a new dimension to achieving economies of scale, especially in the context of digital infrastructure and Banking-as-a-Service (BaaS), laying groundwork for future research on technology’s impact on community banks’ sustainability. Second, it enhances understanding of bank mergers and acquisitions by highlighting how bank size, profitability, credit risk, and demographic factors like local population and income levels affect M&A dynamics, identifying technology investments as a key attractor for acquirers –  a novel insight in the field. Lastly, the findings inform practitioners, including regulators and policymakers, about the importance of technology investment strategies for community banks. As the sector evolves amidst fintech advancements and consolidation, our research underlines the need for supportive policies and frameworks that enable community banks to leverage technology for growth and efficiency, suggesting that strategic technology investments are crucial for their continued relevance and competitive edge.


Shevlin, R. (2021a) Can Banks’ Relationship with FIS, Fiserv, and Jack Henry be Fixed? Forbes.

Shevlin, R. (2021b), What’s Going On in Banking 2021: Rebounding from the Pandemic. Cornerstone Advisors.

Cattaneo, M. D. (2010). Efficient semiparametric estimation of multi-valued treatment effects under ignorability. Journal of Econometrics, 155(2): 138–154

This post comes to us from Cheng Jiang at Boston College’s Carroll School of Management and Jonathan Scott and Zhaowei Zhang at Temple University’s Fox School of Business. It is based on their recent article, “Community Bank Consolidation and the Role of Technology Investment,” available here.

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