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25 Sep 2014

How Analytics Can Transform the U.S. Retail Banking Sector

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[cs_column column_size=”1/2″][cs_image column_size=”1/1″ image_style=”plain” cs_image_url=”http://www.datapanaceaonline.com/wp-content/uploads/2014/09/facturas.jpg” cs_image_url1=”Browse”][/cs_image][/cs_column][cs_column column_size=”1/2″]No matter how you slice it, banking is a data heavy industry. But despite the proliferation of data, effective mining of insights has remained elusive. Given the tremendous advances in analytics software and the processing power generated by cloud-based utility computing architectures, the banking industry is ripe for change.[/cs_column] As the industry works its way out of the financial crisis (amid continued uncertainty over the future), retail banks, in particular, must seriously consider using analytics to improve decision-making, uncover unseen innovation opportunities and improve compliance within a more stringent regulatory environment that is emerging through the Dodd-Frank Act and other impending mandates.

 

These regulations place a high priority on transparency and are pushing banks toward enterprise wide data architectures. This will command a significant (and much-needed) move away from the

siloed approach to computing that has defined banking since the dawn of the digital age, toward a more integrated model in which a single version of the truth is needed to drive business effectiveness and efficiency.

 

Such an approach will power the industry’s push to reinvigorate its relationship with customers. In today’s rapidly changing competitive landscape, regaining customer trust is a top priority for banks as they look to boost revenues and profitability to survive and thrive in uncertain times.

 

[cs_column column_size=”1/2″][rev_slider blog-image][/cs_column][cs_column column_size=”1/2″]Following the economic crisis of 2007–2008, consumers have become more frugal. The age of conspicuous consumption has been replaced by needs-based pragmatic purchasing, a transformation

that pundits interpret as a return to traditional American values. The personal savings rate, which had decreased dramatically in the 1990s, is now showing a small but steady rise.

 

Despite shrinking discretionary spending budgets, consumers (especially those in the millennial demographic) have eagerly adopted new technology, especially smart phones. They have also embraced social networks in big numbers, replacing, in some cases, expensive physical world interactions with a free social variant.

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Their rapidly evolving behavior and preferences cannot be ignored. For banks looking to boost their top lines, these channels offer a simple and powerful way to spread their gospel and build tighter relationships with customers.

 

At the center of this ongoing change is pervasive data — information that banks have possessed all

along but never quite figured out how to exploit. Given that the quality and quantity of data varies

greatly, banks need to prioritize the unique information they hold to accelerate time to insight.

 

By applying new analytical tools and service delivery methods, banks can more quickly convert data into

knowledge to acquire market and service-differentiating capabilities. Such an effort requires the backing

of the organization’s leaders and a cultural shift toward evidence-based decision making.

 

New regulations require banks to provide data that is predictive and risk based. This will require deployment of analytical tools on data aggregated from various business units. Reaching customers effectively via new channels and enhancing the multichannel banking experience will require continuous analysis of the structured customer data residing inside traditional databases and the unstructured bits of data created by customers via mobile phones and social media.

 

In our view, the winners in this unfolding scenario will be those financial institutions that realize the

value of their data and capitalize on it by employing advanced analytics. We believe that banks

should seek to achieve the following through their

Analytics deployment:

  • Predict future scenarios and enhance compliance.
  • Gain insights into what makes them unique and put this insight to use to gain a competitive edge.
  • Drive a customer-centric strategy and improve customer-focused activities.
  • Improve decision-making.
  • Enhance process efficiencies and operating margins by analyzing data to identify inefficiencies.
  • Leverage the emerging analytics-as-a-service model to better manage risk and tap three key resources: people, processes and infrastructure, bundled together to serve as a utility.

Ref: cognizant reports | august 2011