The financial services industry has a vast reservoir of data on their customers, but is in the infancy stage of utilizing this data for financial or competitive gain.
In a study published by the IBM Institute for Business Value in conjunction with the Said Business School at the University of Oxford entitled, "Analytics:
The Real World Use of Big Data in Financial Services," it was found that 71 percent of banking and financial firms globally believe that the use of insight and analytics creates a competitive advantage, compared with 63 percent of cross-industry respondents.
This compares with only 36% reporting this advantage in 2010, representing a 97 percent increase in just two years.
Pragmatic Customer-Centric Strategy
Not surprisingly, the IBM research found that most 'big data' strategies being implemented by the financial services industry begin by initially identifying business requirements, then leveraging existing infrastructure, data sources and analytic
capabilities before incrementally expanding sources of data, technology and analytic capabilities. This 'slow to go' progression is actually on par with the global cross-industry counterparts reviewed.
It should be noted that the progress with almost any data initiative in the financial services industry is directly correlated to the size of organization due to the investment required and current infrastructure of the organization.
Despite the size of organization surveyed, and reinforced by
Celent research, customer-centric objectives dominate the focus of most data activities in the banking industry. In fact, 55 percent of active data efforts revolved around customer outcomes in the IBM study.
Focusing on the customer is increasingly important as channels for transacting and communicating continue to increase, developing new segments of customers based on the ways(s) they want to perform transactions and hear from their bank and credit union.
Through this customer-centric focus, the customer experience should improve as financial institutions can better anticipate customer needs in a multichannel environment.
Second in importance for financial organization use of data was for fraud and risk mitigation and achieving regulatory and compliance objectives (23%). This focus was significantly higher than the cross-industry sample in the study.
The study also found that, while the majority of institutions surveyed said they had much of the infrastructure in place to manage the increasing flow of data (87 percent), only slightly more than half reported that their data was integrated across silos.
This continues to be a challenge, as customers expect their financial organization to understand their entire relationship when working with their bank or credit union. This challenge is obviously exacerbated with smaller organizations that may not even have
a CRM system in place.
Focus on Internal Data Opportunities
Despite industry and solution provider hype, most early big data initiatives are focusing on analyzing the tremendous amount of untapped opportunity that still resides within most financial institutions. More than 4 out of 5 financial organizations surveyed
in the IBM study are analyzing transaction and log data that has been collected for years, yet not analyzed due to system constraints.
Where banks and credit unions lag their cross-industry peers is in using more varied data that requires more sophisticated (and expensive) technology. For instance, while call centers are still very important to financial institutions, only 21% of larger
banks analyze this data (compared with 38% on non-financial organizations). Financial institutions also significantly lag their cross-industry counterparts in evaluating social data (27 percent for banks compared to 43 percent for non-banks).
Analytic Capabilities Lag Non-Bank Counterparts
Data mining of structured internal data such as basic inquiries, predictive modeling, etc. is on par with other industries. There is a significant drop off in capabilities, however, when financial institutions are asked about the ability to analyze unstructured
data such as voice and social streams.
While the investment in these types of analysis should lag the basic capabilities described earlier (analyzing internal, structured sources), the growth and power of advanced analytics that includes unstructured data needs to be tested by banks to
determine monetization opportunities (ROI).
Go Forward Recommendations
Advancing technology in combination with vastly expanded data sources are combining to provide the foundation for tremendous advancements in the application of big data insights within the financial services industry. Despite this potential, however, even
the most advanced organizations are following a very structured path of integrating data analytics and insights within the organization.
In writing and speaking on the subject of big data for more than two years globally, I have found that much of the hype surrounding 'big data' has significantly preceded the
proven financial benefits of using all of the data available to banks and credit unions. Unfortunately, many organizations still believe they are required to play 'catch up' to the minority of organizations that have the resources and talent to conduct
an expansive test and learn process around unstructured data.
Without regard to resource availability, here are some foundational common sense steps that the IBM study, Celent research, other studies in the financial services industry (and myself) believe should be taken before expanding capabilities around big data.
- Begin with initiatives that will have a proven financial impact of increased revenues and/or decreased costs (increased sales, lower cost delivery, enhanced service, reduced risk)
- Build a blueprint that aligns business needs with IT capabilities (and resource requirements)
- Engage all impacted parties (executive level buy-in is required)
- Start with internal data sources (logical, cost effective and with great upside potential)
- Apply a test and learn process for all initiatives with measurement applied against preset objectives
Big data provides the potential for big opportunities for banks and credit unions. But the definition and application of 'big data' should begin with small steps applied against internal data that is readily available. As successes are achieved, the financial
and operational benefits and learnings can be applied towards more ambitious projects that are deemed to be financially viable.