Banking on Big Data and Analytics

By Omer Sohail

Nobody knows data the way banks do.

For years, financial institutions have collected reams of information on their customers: the transactions, account activity, loan portfolios and credit card balances that are their lifeblood. And they convert much of this data into monthly, quarterly and annual reports for use by their customers, risk managers and auditors.

For the most part, banks have done a good job of looking back in time and leveraging historical data. Looking forward? That’s more of a challenge.

Traditionally, departments within banks have operated in silos, meaning that many institutions have no clear, single view of their customers. Thus, careful analysis—by individual or household—of account usage tendencies, receptivity to marketing programs, customer service preferences or demographic patterns is nearly impossible.  However, there is a lot of focus on getting this enterprise view of the customer fixed.

Furthermore, besides leveraging traditional predictive analytics, many of the world’s largest financial institutions have started to eagerly tap into big data and predictive analytics to improve the customer experience, bolster incremental revenue or better manage risk. Analytics-enabled banks are seizing on a golden opportunity by using structured (existing customer and account profile data), semi-structured (social media content) and unstructured (customer support audio files, customer transactions) data to generate incremental business, reverse attrition trends, meet stringent regulatory requirements and establish more meaningful customer relationships.

A 2012 survey conducted by Deloitte of executives in the United States and other countries found that 96 percent of respondents (many of them bankers) believe the use of analytics will increase in importance over the next three years. Although the use of analytics as a marketing tool lags behind other disciplines, 55 percent of respondents said their organization’s marketing and sales groups are investing in analytics today—a number that’s expected to rise.

To move forward, banks should transform their approach to data and analytics from traditional, product-focused marketing programs to a 360-degree customer-focused approach. They should also strive to break down longstanding silos, invest in state-of-the-art IT systems, hire business-savvy quantitative analysts and data scientists, and integrate their operations to see the real potential in data.

For banking analytics to be truly effective, it should help banks operate horizontally across the enterprise, rather than narrowly, viewing one business unit at a time. This is often difficult because infrastructures were designed to work vertically. The 360-degree view of customers that analytics enables is essential for meaningful customer relationships and business growth.

Automating Informed Decision Making

Analytics can provide banks with more marketing muscle. The ability to perform complex statistical analysis—automated and in real-time—can produce desirable, personalized offers at precisely the moment the customer is ready for it. Without a robust analytics platform that can act expeditiously, that sales-ready customer may quickly move on, and that’s an opportunity lost, perhaps forever.

As one executive from a global bank remarked in Deloitte’s Analytics Advantage survey, “I would rather have a little bit of the right information at the ‘moment of truth’—when a customer is ready for it—than all the information in the world two weeks later.”

Perhaps most importantly, a well-designed and well-managed data analytics program can enable banks to generate more multichannel customer journeys. These encounters, designed from thorough analyses of customer transaction data, can enable banks to offer a satisfying experience regardless of how the customer chooses to interact.

Consistent yet personalized multichannel customer journeys—via call centers, branch visits, ATMs and online—can lead to satisfaction, loyalty and incremental business through innovative new products and services.

In addition to the significant benefits big data brings to the customer experience, this technology can also be used to generate cross-organizational efficiencies. For example, many banks use analytics to construct and implement a thorough compliance management program.

New or revised regulatory requirements, accounting standards and risk management practices can now be automated and streamlined, thereby reducing or eliminating cost and administrative burdens. Fraud detection and supply chain improvements can also benefit from prescriptive analytics-driven programs.

Automated decision making and other analytics-driven competencies have a track record of generating higher revenues in a faster time frame and at a lower cost. These tangible results are opening the eyes of even the most conservative executives, who are prying open their vaults for additional investments in big data that can positively affect financial performance. Successful trials with impactful ROI can swiftly build out an analytics budget.

Banks are increasingly embracing the new world of big data and analytics, serving as a bellwether for other consumer-facing industries eager to similarly create a definitive single view of each customer, serve and retain them, attract additional business, better manage risk and mine new revenue streams.

Omer Sohail leads Deloitte Analytics efforts for Financial Services, with the majority of his time spent with leading banking and securities clients.  He advises CxOs on how to increase revenue, manage risk, address regulatory compliance, reduce cost or obtain competitive advantage by streamlining data infrastructures, implementing analytics modeling and operationalizing decision management.

As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.

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