Banking Industry Still Taking Small Steps with Big Data

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.

The vast majority of banking and financial firms globally believe that the use of insight and analytics creates a competitive advantage. The industry also realizes that they are sitting on a vast reservoir of data and insight that can be leveraged for product development, personalized marketing and advisory benefits.

in the past, most ‘big data’ strategies being implemented by the financial services industry have begun by initially identifying business requirements, then leveraging existing infrastructure, data sources and analytic capabilities before incrementally expanding sources of data, technology and analytic tools. This ‘slow to go’ progression, while viable in the past is not keeping up with consumer expectations on how they want their financial partners to partner with them.

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, however, customer-centric objectives dominate the focus of most data activities in the banking industry. In fact, according to the Digital Banking Report, Improving the Customer Experience in Banking, over 80% of financial services organizations globally indicated that customer experience was a top 3 priority.

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.

Financial organizations also must use data and advanced analytics for fraud and risk mitigation and achieving regulatory and compliance objectives. With cybersecurity more important than ever, falling behind in the use of data for security purposes is not an option.

While the majority of institutions might have much of the infrastructure in place to manage the increasing flow of data, significantly fewer have their data integrated across silos. This continues to be a challenge as customer 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 who 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. Most of this data has been collected for years, yet not analyzed due to system constraints. Unfortunately, while this data already collected is an important foundation, the leveraging of much more powerful channel, social and behavioral data is not at the forefront of most discussions since consumers are more interested in what they should do in the future as opposed to being told what has already occurred.

For instance, while call centers are still very important to financial institutions, few organizations analyze this data. Financial institutions also significantly lag their cross-industry counterparts in evaluating social data and channel specific data including locational insight.

Analytic Capabilities Lag Non-Bank Counterparts

Most research studies have found that 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 globally for years, it is clear 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 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.

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