Big data and analytics redefining banking industry

Big Data and Analytics – Redefining Banking Industry

August 2014

Big Data and Analytics – Redefining Banking Industry Overview Post the financial crisis; banks are facing a much tougher and challenging operating environment.

Customers have become tech-savvy, demanding and less loyal to their bank. New regulations continue to loom, requiring granular governance and recurrent control. Questions have been raised about banks’ ability to understand risk well and act on insights. Scarcity of capital requires a tighter focus on operational efficiency and decisions that are both risk-informed and capital-adjusted. To top it all, the competition for profitable returns in this marketplace is extreme.

In their fight for survival, banks are relying on a new weapon – big data and predictive analytics. Many banks have started to eagerly tap into the potential of big data and predictive analytics to improve customer experience, bolster incremental revenue, and better manage risk. The banks’ tryst with big data does not end there. By analyzing reams of structured and semi-structured data, the banks will be able to generate incremental business, reverse attrition trends, meet stringent regulatory requirements and establish more meaningful customer relationships.

This report delves into how banks are adopting Big Data and Predictive Analytics to not only remain relevant but also stay ahead of the competition, by providing personalized customer service.

Big Data in the Banking Industry Big data is a massive volume of structured, semi-structured and unstructured data that can be harnessed for information. It requires new technologies and processes to store, organize, and retrieve these large volumes of data.

Increasing customer base and their use of mobile and other emerging technologies has seen a surge in transactions, leading to quick generation of huge amount of data for banks on a daily basis. Analysis of this data presents great prospect to the banking industry.

By using structured (existing customer and account profile data), semi-structured (social media content) and unstructured (customer support audio files, customer transactions) data, big data-enabled banks are catching an opportunity to generate growth in business, reverse attrition trends, meet stringent regulatory requirements and create more meaningful customer relationships.

In the US, several big banks such as JPMorgan Chase, Bank of America, Citigroup and Capital One are using big data mainly to understand how customers use their different channels, such as branches, online, mobile, call centers and ATMs. Recently, Wells Fargo set up a Big Data lab to get more insights on customers and prevent fraud.

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Case Study on Wells Fargo Preimplementation Stage

Wells Fargo, the fourth largest bank in the US, had huge amount of electronic data on its customers for decades, benefits of which it realized in the past few years.

Implementation Stage

In 2012, the bank set up a ‘Big Data Lab’ to use emerging technology and data science to drive customer experience, prevent fraud and develop customer insights.

The bank’s data analytics team integrates data sets (from both internal and external sources) and presents the results in a customized dashboard format to all managers.

Postimplementation Stage

Through its innovative practice, Wells Fargo looks five years or more into the future and tries to understand how technologies are changing outside of financial services and also outside of the US, as well as how demographics are changing.

Managers use this service to make better decisions, present data on an ad-hoc basis at meetings, and self-serve their specific research interests using a number of additional data visualization tools.

Though big data presents significant opportunities, executing these projects with several technologies available remains a big challenge for the banking industry. Firstly, integration of big data with a bank’s legacy systems is a daunting task. Secondly, gathering heaps of data from various sources and channels is a very time-consuming and costly proposition. These challenges are discussed in the below case study of Deutsche Bank, which faced difficulties in implementing big data due to its legacy systems.

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Case Study on Deutsche Bank

Overview

Challenge

Process

Deutsche Bank, a German-based leading global banking and financial service provider, has undertaken the big data implementation project since the start of 2012 to analyze all of its unstructured data.

The bank experienced various problems while deciphering the conventional systems — mainframes and databases – and trying to make big data tools work with these systems. Given the data integration challenge and the key investments made by the bank in the traditional IT infrastructure, the bank’s senior executives wondered what they should do with the traditional system.

The data integration challenge and the key investments made by the bank in The bank has been gathering data various sourcesforand – the traditional IT infrastructure pose an from important question thechannels bank’s senior executives – what should their traditional front end (trading data),they the do middle (operationssystem? data) and the back end (finance data). This data, running into petabytes, is stored across 46 data warehouses, with 90% overlap.

It is difficult to untangle these data warehouses that have been built over the last two to three decades.

Analytics in Banking Big data is a very broad term, which includes extensive management of data, including unstructured data (using text mining techniques, etc.). Analytics in "Big Data" includes conventional decision management techniques such as identifying patterns in data, clustering observations and forecasting.

Analytics provides banks complex statistical analysis in real-time – which can produce desirable, personalized offers at the time the customer is ready for it – and gives banks more marketing power.

Banks are focusing on investing in emerging technologies such as automation of predictive analytics modeling, customer analytics, real-time offer engines for customer acquisition, data analytics, real-time personalization of customer experience, etc. Many banks in the US are building their own analytics center. Also, intense competitive pressure is forcing bank executives to build their own analytics backoffice groups to have an edge over their competitors. For example, KeyBank, a Cleveland-based bank, created a centralized Client Insights Center of Excellence, leveraging analytics to deliver its customer relationship strategy and better serve clients.

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"Now all decisions have to be Net Present Value positive; someone would have to make the case in data terms. We use analytics to force discipline in how to think about the right answers for our customers and for the bank." – David Bonalle – EVP and Director of Client Insights and Marketing, Key Bank

Conclusion To meet new challenges and remain competitive, banks are going the big data way. Big data is helping banks transform massive volumes of organizational data into actionable insights and strategies. By implementing big data and predictive analytics, the banks are shifting from conventional, productfocused marketing programs to a 360-degree customer-focused approach. Big data offers manifold advantages but to unleash its real potential, the banks need to start investing in state-of-the-art IT systems, hire business-savvy quantitative analysts and data scientists, and integrate their operations.

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