Transformation of Financial Industry through Data Analytics

Alan Royal, Head of Technology Innovation and Business Transformation, Strategy CIO

Alan Royal, Head of Technology Innovation and Business Transformation, Strategy CIO

Ex-Barclay's CEO, Antony Jenkins, recently said incumbent banking institutions “… risk becoming merely capital-providing utilities that operate in a highly regulated, less profitable environment” in addition to suggesting “a series of Uber-style disruptions in the industry could shrink headcount at traditional big banks by as much as 50 percent,” as well as concluding “In my view only a few (incumbent banks) will have the courage and decisiveness to win in this new field.”

“Data analytics, directly stated, is the engine which enables differentiated digital consumer experience “

Despite my annual technology forecast predicting “differentiated digital distribution” to be a material differentiator for the financial services industry, and more specifically banking, what has emerged is far beyond my expectations. Early 2016’s announcement of Quicken Loans “Rocket Mortgage” (a total end-to-end online mortgage fulfillment process), and MasterCard announcing fingerprint, and facial recognition security authentication biometrics, dramatically accelerates the need for banking “differentiated digital distribution offerings”. This declarative reminds me of the long held metaphor of “easier said than done”.

The implications for banks are material. Sub-optimal digital experience can result in current consumer dilution, vs. a unique and differentiated digital consumer experience, which drives new consumer acquisition. Thus, the emergent banking differentiated battleground, will be in the “virtual world” of differentiated digital distribution. Personalized, behavioral driven consumer specific predictive interaction models will drive differentiation.

Banks need to drive their current market differentiation into a tangible digital distribution enabled future state, which fully embraces a market driven, differentiated behavior based, predictive interaction models, relevant to specific demographic groupings, which drive current and visionary market facing differentiators.

Also, deeply embedded, is the requisite “data engine” which either enables or disenables organizational success. Large organizational legacy systems tend to fragment, rather than consolidate, consumer relevant data. As a consolidated consumer by consumer data record, is a single point of failure, the associated tactical data consolidation is an essential component of success. This consolidation of consumer data (often referred to as Big Data) enables data scientist to inform, the future state behavior and predictive elements, of the overall consumer future state experience.

This capability requires the creation of a data warehouse, which provides the necessary data to establish customer behavioral preferences, as well as predictive analytics, which anticipates likely transactions a customer will be most likely to initiate. In addition to the data warehouse, a team of data scientists is required to perform the actual constant stream of behavioral and predictive data analytics required to fulfill solution design data requirements.

For banks, enhancing consumer data retention and consolidation is essential, to improving the data analytics’ driver behind any customer specific, behavior, and associated predictive analytics. Banks need to establish their unique data consolidation and enablement strategy, by considering: current data retention, which is extractable, specific data variables available, length of data retention, quality of data retained, etc. Often the available bank data has to be supplemented, though the purchase of consumer specific data from third party data sources, which are now broadly available.

The hiring (contracting) of data scientist’s is essential. These individual’s participant into what data is mined from existing systems, how the data is organized, thus allowing them to determine if supplementary externally sourced third party data is needed, based upon determining if internal data sources alone provide the necessary behavioral and associated predictive analytical outcomes need to drive a banks unique customer behavior and predictive digital experience. The final outcome from these activities is an initial analytical database.

The ultimate digital experience for banks is omni-channel transaction experience, which provide for the same customer transaction experience, regardless of where/how the consumer experience is initiated. Implicit, is the streamlining and consolidation of the number of transaction channels. Enablement of this capability requires internal organizational process re-design, as well as material consolidation of current transaction process streams. If a customer wants to initiate a transaction on a Laptop, iPad, Android, The Cloud, Etcetera, the device type should have no impact on the overall customer experience.

The good news is that core enabling technology, data, and business strategy assets are likely in place; but the challenge is how to accelerate the application of these assets into organizational “differentiated digital customer assets” implemented to maintain, and or expand, organizational market share.

In order to enable and expedite the data scientist’s, activities, banks need to, almost assuredly, purchase purpose-built solution software, providing the data scientists analytical tools, for baseline data analytics, which maximize the banks unique, customer specific, behavior and predictive analytical footprint, in the most expedient manner.

The number and magnitude of required transformational initiatives, required to enable above, is a daunting material undertaking for banks. However, growing customer demand for a, “customer tailored predictive differentiated digital experience” must be addressed by banks, in an expedient manner, to retain current customers, as well as enable new customer acquisition strategies. The key to delivery of this, bank specific, differentiated digital interaction model, is tied to a top to bottom, organizational synergistic delivery approach, as the “sum of the whole, is greater than their parts”.

The key strategy for banks in 2016, is to deliver the most differentiated customer specific digital experience, in the most expedient and cost effective manner, through the leverage of organization wide enabling assets. Technology brings to the solution offering identified quick wins, related to bank specific emergent behavioral and predictive customer specific analytic solution assets. Business brings to the solution, the offering of bank specific customer marketing differentiation drivers, as well as business staff, which can actively participate in, and help expedite solution delivery, through unparalleled technology and business solution delivery interaction. Top management, and the board, oversee and provide funding for expedited project delivery, while in parallel, disenable any bank specific political barriers. In essence, senior bank leadership provides project air traffic control, diverting any barriers in flight, which could negatively impact solution delivery takeoff.

As you now read a number of publications regarding different dimensions and insights into how data analytics is approached from a number of writer’s, you now understand the importance of this topic as it relates to enabling, bank specific, differentiation in the evolving digitalization of consumer experience. Data analytics, directly stated, is the engine which enables differentiated digital consumer experience.

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