Data Intelligence for Financial Services

Analyst Viewpoint

Darwin never claimed that evolutionary success came down to survival of the fittest. To the contrary, he stated, “it is not the strongest that survives; but…the one that is able best to adapt and adjust to the changing environment in which it finds itself.” The long list of once thriving businesses that failed because they lacked the agility to evolve as rapidly as their markets is proof that this lesson applies equally to banking and broader financial services, and an inability to quickly read and respond to market signals is a common cause of that failure. External data includes information about competitors, locations, customers and the world outside an organization such as economic and market insights. Financial services organizations that integrate external data into all their analysis, planning and performance reviews are better able to detect and respond to their environment and by doing so, to survive and thrive.

Financial services organizations that integrate external data in their analysis, planning and performance reviews are better able to detect and respond to their environment and by doing so, to survive and thrive.

Business competition is a matter of us-versus-them, so why limit analysis, planning and performance reviews to internal comparisons such as actual-to-plan and percentage changes from a prior period? Why celebrate a 7% increase in return on assets against an expected 5% rise when the market grew by 12% and, in reality, the company experienced a market share loss? To remain competitive, a comprehensive set of external data is no longer a “nice to have” item. External data is necessary for useful and accurate business-focused planning and budgeting, and for performance benchmarking. Consider the example in retail banking of a location that serves a demographic that is more digitally engaged and that does not like to visit physical locations. Using only internal data on the visitors and transactions at a location could lead to erroneous conclusions about that location’s performance. Including external data such as demographics and customer location can provide more valuable insights for improving outcomes. This is becoming increasingly important as financial services organizations adopt artificial intelligence (AI) using machine learning (ML), because omitting external data in modeling will result in flawed models with potentially expensive negative consequences.

External data can be used in all types of planning. It may be highly detailed, tactical planning performed by operations managers to support a site planning process, or long-range planning that drives capital investment, strategic investments or marketplace assessments. Incorporating external data benefits the entire organization, including the senior leadership team, finance and operations, and business analysts working in operational roles. External data can improve the accuracy of projections.

Constructing and using business and financial planning models based on a more robust set of external data can result in more accurate forecasts and plans that are more adaptable to change. This in turn can improve a company’s operating and financial performance. However, until recently, financial services organizations have included little—if any—outside intelligence because it was not readily accessible in the time available. Relying solely on internal numbers is a habit that must be broken because there are practical and affordable alternatives. Today, there are services that supply a range of data from general economic data to data on specific industries and even on specialized processes. Some of these services have worldwide coverage while others may focus on specific geographies. Some data providers offer only historical numbers while others offer forecast data services that use proprietary methods to project, for example, demand for specific products in specific markets.

This wealth of available data poses a new issue for business and financial planners because they must identify the data most relevant to their particular requirements before incorporating it into a model. And realistically, the acquisition and use of external data must fit within existing planning processes. Although some financial services organizations may have the in-house ability to determine which sets of data fit their needs, others will need third-party advisory services to help operationalize their models so that they accurately reflect the interaction between external dynamics, internal operations and financial constraints. Today's focus on digital transformation will expand the use cases for external data. For example, using analytical systems to continuously monitor external feeds and alert executives or managers when the data suggests that their demand and service chain are out of step with reality. These alerts enable more timely course corrections to mitigate risk or seize an opportunity.

Business challenges never really change because financial services organizations must always attract customers, hire and retain talented staff, and operate efficiently and profitably. What does change are the tools available and techniques that will provide a competitive advantage. This is especially true in the areas of forecasting, planning, analyzing and reviewing because using external data to generate predictive insights from data intelligence can provide a financial services organization with such an advantage by being able to plan more accurately and adapt to change faster.