Data Considerations for Successful Deal Integration

Bank M&A activity is heating up in 2021; already, a number of banks have announced deals this year. Is your bank considering a combination with another institution?

Banks initiate mergers because of synergies between institutions, and to achieve economies of scale along with anticipated cost savings. Acquiring institutions typically intend to leverage the newly acquired customer base, but this can be difficult to execute upon without a data strategy.

Whether your bank is considering buying or selling, it has never been more important to evaluate whether your data house is in order. Unresolved acquisition data challenges can result in poor customer experiences, inaccurate reporting and significant inefficiency after the merger closes. What causes these types of data challenges?

• Both institutions possess massive volumes of data and multiple systems, while disparate systems prevent a holistic view of the combined entity. In a merger, the acquirer does not have access to the target’s data until legal close, and data is not consolidated until the core conversion is completed.
• Systems are often antiquated, and it is difficult to access high-value customer data. Data integrity is often an issue that impedes anticipated synergies that could promote revenue generation.
• Absence of enterprise knowledge or insight into target’s customer portfolio. This makes it difficult to identify growth opportunities and plan the strategy for the combined institution. It also creates a barrier to pivoting in the event a key relationship manager leaves the institution.

Baltimore-based Howard Bancorp has conducted five successful acquisitions in the last eight years. Steven Poynot, Howard’s CIO, recommends looking internally first and getting your house in order prior to any merger. “If you don’t understand all of the pieces of your bank’s data and portfolio well, how are you going to overlay your information in combination with the other bank’s data for reporting?”

Five solutions to merger data challenges include:

• Create a data governance strategy before a deal is in the works. Identify the source and location of all pertinent data. Evaluate whether customer data is clean and up to date. Stale customer information such as old land line phone numbers and inaccurate email addresses yield roadblocks for relationship managers attempting to use data effectively. If your bank does identify data issues, implement a clean-up project based on a data governance policy framework. This initiative will benefit all banks, not just those looking to merge.
• Develop an M&A integration plan that sets expectations and goals. Involve the CIO quickly and identify tools needed for the integration. Make a strategic determination of what data fields need to be integrated for reporting purposes. Acquire tools to allow for enterprise reporting and to highlight sales opportunities. Partner with vendors who understand the specific challenges of the banking industry.
• Unify Disparate Systems. Prioritize data integration with a seamless transition for customers as the top priority. Plan for mapping and consolidating data along with reporting for the combined institution. Take product and data mapping beyond what is needed for the system mapping required for core integration. Use the information gleaned from the data to support product analytics, risk assessment, business development and cross selling strategies. The goal is to combine and integrate systems quickly to leverage the data as an asset.
• Discourage Data Silos. Make data available and easily accessible to all who need it to do their jobs. Banking is a relationship business, and relationship managers need current customer relationship information readily available to them.
• Analyze. Once the data has been consolidated, analyze and leverage it to identify opportunities that will drive revenue.

In a merger, the sooner that data is combined, the earlier decisions can be made from the information. As data silos are removed and data becomes easily accessible across the organization, data becomes an enterprise-wide asset that can be used effectively in the bank’s strategy.