Data Analytics for Financial Institutions

The Journey from Insight to Value

By Sushil MalhotraBerk HizirMohammed Badi, and Alenka Grealish

Savvier data analytics practices can help financial institutions (FIs) extract value worth billions of dollars. That’s the core finding of a new BCG study, prepared in partnership with Morgan Stanley Research. The study—based on interviews with data analytics executives and experts, a complementary survey, and joint analyses by BCG and Morgan Stanley—revealed that the financial services sector trails most industries when it comes to successfully generating value and actionable insights from data analytics.

FIs continue to collect vast amounts of data, but few are generating a return commensurate with their investment. The potential value of better data analytics—as a result of higher revenues, cost savings, improved customer service, and better decision making—could reach as high as $30 billion. Players that embrace leading practices will be able to grab a significant slice of that pie, while those that lack the required capabilities will have little choice but to fight for the remains and be threatened by disruption.

Among the issues holding back many FIs in the sector is a lack of actionable data sets, the kind needed to provide tailored, real-time offerings and advice. Moreover, many FIs do not have the breadth and depth of data in a given customer segment or product area to identify and target specific customers. Entrenched cultures and operational complexity can also make it difficult for institutions to adopt the rapid test-and-learn approach required of the most successful data and analytics initiatives.

Our study revealed five actions that FIs can take to address these issues and release the untapped value in data analytics.

  • Engineer interactions that generate a fair-value data exchange with customers. FIs have extensive internal data. But they are often missing the kind of unique data that sets an FI apart from its competition by unlocking specific, actionable insights. These unique data elements can be about specific individual preferences or real-time data (such as location). To acquire them, FIs need to engineer digital customer interactions that have a clear value proposition for customers.
  • Create an ecosystem of partners to generate value at scale. By partnering with merchants and other companies across the financial services sector, institutions can tap into a much deeper base of customer and product information. Such mining not only fills a key void for most financial players but also allows them to collaborate with partners to build a richer variety of data-driven offerings that they can then offer to customers. Card-linked offer programs are one example, but much more can be done, especially in fraud detection and prevention.
  • Establish a center of excellence to improve coordination and leadership. To cut through the internal complexity common at most FIs, some top performers have begun to apply a center of excellence model. One leading bank took this approach to bring greater cohesion to its data and analytics initiatives. To ensure buy-in and agreement, the center worked with dedicated liaisons for each of its major business and operational lines. The bank also made sure that the center had wide visibility at the enterprise level, the committed backing of senior leadership, and the opportunity to roll out customer data initiatives in a streamlined manner across the business. Multifaceted teams of data engineers, data scientists, and business intelligence analysts were key to the bank’s success. The center of excellence model pooled these skill sets and internalized the fast-paced culture of innovation that was needed.
  • Take informed risks and refine continuously. Calculated risk taking and rapid product development are hallmarks of digital leaders such as Amazon and Google. FIs can take a page from the same playbook to accelerate innovation around data analytics. A tighter and more effective customer feedback loop is critical. Knowing what customers want and ensuring that new data-enabled offerings successfully hit the mark require rigorous and ongoing test-and-learn cycles. To succeed, FIs must adopt the practices of leading digital players, giving employees the permission and tools to refine customer interactions on the basis of continuous, data-driven feedback loops.
  • Make trust an asset. Our study found that two-thirds of the total value of data analytics depends on earning customer trust. Organizations that proactively manage customer privacy will therefore be best positioned. FIs that share their data policies with customers, restrict the use of customer data to agreed-upon applications, and provide clear and tangible value will create better products and faster growth.

We estimate that the effective application of data analytics could deliver an earnings-per-share improvement of 5% from cost savings alone, with long-term revenue generation opportunities bringing the prospect of even higher returns. FIs that move swiftly have the potential to gain a substantial competitive advantage over their peers.
To learn more about turning large, complex data sets into insights and competitive advantage, see BCG Gamma and BCG's approach to big data and advanced analytics.

Data Analytics for Financial Institutions