How to Become a Data-Driven Bank

May 8th, 2017

data-5-8-17.pngBanks collect lots of data on their customers, but they aren’t always adept at using it to grow their business. Community banks, in particular, are just beginning to realize the power of data analytics and business intelligence.

Client data and the tools to analyze it can transform how banks conduct their commercial lending business. Data-driven banks can leverage analytics to make better informed decisions, streamline operations, and improve customer service.

The following are three steps for boards to consider for successful adoption of better data analytics:

  1. Support investment in systems that organize and centralize data and standardize processes.
  2. Reinforce the systems investment with policy, training and change management initiatives.
  3. Champion the new systems and processes and how they contribute to the bank’s success.

Here are some practical recommendations for a community bank executive who wants to turn data analysis into bottom-line results.

Define the data universe. The data that community banks can use includes company financials, qualitative customer data, and borrower behavioral data, including payment and credit utilization history. Establishing a centralized system that captures this unstructured data consistently is the first step in this process.

Consider a partnership. Effective analytics strategies ensure that short- and long-term goals are aligned with the bank’s current business operations. Partnering with a vendor with the required analytics technology and implementation expertise could help the bank capture the right data and integrate it into their processes.

Data quality is key. The top tactical issues with this approach involve collecting, organizing, and protecting the quality of the data. Maintaining the integrity of analytics requires clean data that is accurate, comprehensive and continually updated. Data quality is key to realizing the value of business intelligence tools.

Communicate early and often. Educating the organization on the value of credit measures, whether back office risk managers or front office sales professionals, will equip all stakeholders with a solid understanding of the new analytic tools and how they support the overall goals of the bank.

Establish Success Metrics. Even data-driven banks should be wary of aligning internal data with external benchmarks and best practices, because the latter may not be applicable to a particular type of business, product focus, marketplace or strategy. Instead, banks can use internal data to define their own benchmarks and measure success against goals and past performance. Assessing actual performance by comparing historical trends to new profitability, default and recovery metrics (including internal ratings) serves as an indicator of improvement. In other words, how would the prior portfolio perform given new tools and measures versus its actual performance?

Leveraging advanced data analytics and business intelligence tools is an investment that, if properly implemented, should pay dividends in the form of higher quality loans, better customer service and increased operational efficiency.

To read the complete white paper, “How to Become a Data-Driven Bank,” click here.

nmichael

Nancy Michael is a senior director for Moody's Analytics.

mraissi

Mehna Raissi is a senior director for Moody's Analytics.