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Improve Bottom Line Results through Better Data Management in Banking

Banks are an Information Enterprise

Banks are an information enterprise. All decisions that impact profitability, capital adequacy, liquidity, risk management, credit, compliance, customer experience and the treasury functions are based on a data foundation. Having a common data foundation is essential to the success of a banking enterprise.

A common data foundation can help improve Net Interest Income. According to a recent Boston Consulting Group study, having key bank functions such as finance, treasury and risk use the same common source of data for analysis can increase Net Interest Income by 10-15%.

Examples of Data Driven initiatives in banking includes the following:

A key best practice in banking data management is to create a Shared Service in Data. Recognize that many key initiatives from different parts of the bank are data intensive and often use the same data. Pulling data once and using it many times will save money, improve bottom line results and customer service, reduce risk and fraud and importantly – set the bank up to control its own future.

Benefits of Having an Enterprise Data Program

Some of the benefits from having an effective enterprise data program include:

  • Lowering the capital cost of internal projects. Many key initiatives in the bank use the same data. Often these initiatives pull and scrub their own data. Gathering and cleaning data can be up to 80% of the cost of a project. Many projects tend to share the same data. The illustration below shows this overlap at a high level.

For instance, there is significant data overlap between BSA/AML and Marketing Analytics. However, they are often separate data environments without reusability between the two. Likewise, with Asset/Liability Management, CECL and Stress Testing. All very similar data.

Each initiative could be implemented faster and cheaper with an enterprise data environment.

  • Implementing Digital Banking Capabilities. Data, especially a 360 view of a customer, is a foundation stone to digital banking. Being able to access data on a real time basis is key to driving the digital enterprise forward

  • Competing on Customer Experience. Being able to resolve customer problems quickly and across multiple channels (i.e. call centers, mobile, branch) requires quick access to the same data.

  • Identifying Opportunities for Profitable Growth. Identifying new opportunities with commercial and retail customers is a data driven initiative. Often it requires the capability to integrate bank data with external sources of data and perform the correct analytics.

  • Satisfying the Regulators. Being able to trace key reports and performance measure to a reliable source of data (data lineage) is a critical capability for banks. In times of banking stress, this is especially important.

A Strategic Data Roadmap is Essential

The best run banks recognize that effective data management is a key competitive advantage for them, and an enabler to grow their business, manage risk, improve customer retention. Strategic technology investments in data management & data initiatives are consistently in the top one or two spots for banks, according to American Banker magazine.

On average, Forbes estimates that the large banks are spending about .5% of assets in general on technology, while smaller banks are spending about half of that number (.22%). For smaller banks in the long term, this disparity in spend on disruptive technologies could become a significant competitive disadvantage.

So, how will a smaller or mid-sized institution compete and can it? We think the answer is resoundingly yes, but the smaller institutions will need to be strategic, smart and selective about how they make their investments, and will also need to change some internal behaviors.

A Strategic Roadmap helps align the Executive Leadership of the bank around the key objectives of the Data, Analytics & AI program. Below are examples of the types of initiatives that banks generally focus on in their Data, Analytics & AI Strategic Roadmap.

A Strategic Data Roadmap:

  • Clearly enables the achievement of the key business objectives of the bank

  • Recognizes Data as a shared enterprise asset that must be governed by the business

  • Gets the most strategic value out of the technology investments

  • Aligns the Executive Leadership around the plan, sponsorship and business case

  • Defines the role and responsibilities of Business Data Leadership and Information Technology

  • Defines a detailed plan for success.

The good news is that for smaller banks, there are a number of lessons learned and mistakes made by the larger banks that smaller banks can exploit to their competitive advantage. Although many banks have made investments in data and analytics, they have not always made smart investments.

Data Governance Makes the Data Strategy Work

The good news for midsized and smaller banks, is that there are a number of lessons learned and mistakes made by the larger banks that smaller banks can exploit to their competitive advantage. Although many banks have made investments in data and analytics, they have not always made smart investments.

It’s critical that an effective Data Governance process be put in place to help drive the effective and efficient use of data in the bank. Data is the foundation for AI & Analytics. The Data Governance team can help ensure that the same data is being used across key initiatives, data quality and lineage are improved, and that the bank is ready to exploit data to its strategic advantage.

Our next article will take a more in-depth look at how to implement an effective Data Governance program

At FinResults, we can help your bank develop and implement a Data, Analytics and AI strategy that will bring strategic value to the firm and help it become:

  • Smarter – at identifying and understanding new opportunities

  • Faster – at bringing new products to market and improving processes

  • Better – at managing Compliance, Risk and the Customer Experience

  • More Efficient – Lowering your cost of operations and making more efficient use of data investments.

To get started, please reach out to me at

About the Author:

The author is Michael (Mike) Andrud, President of FinResults, Inc. Michael can be reached via email at or at +1 (561) 288-6548. Previously Michael was a mid-sized bank Chief Information Officer (CIO), Chief Data Officer at a large bank, and Principal Banking Data consultant at a large consultancy.

FinResults is a consulting and technology professional services firm located in Boca Raton, FL, focused on helping smaller and midsized firms succeed in Digital, Data and Process Transformation.

This article and all diagrams are the © copyright protected materials of FinResults, Inc.


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