A Study to analyze the effectiveness of using Big Data Analytics in Banking Industry

After the Lehman Brothers collapse and the Subprime Crisis in 2007, the global banking industry has gone through a turmoil followed by a rapid transformation. As per a report published by Cap Gemini in 2013, most of the banks across the world are now focusing on fulfilling regulatory compliance and improving their asset quality. The challenges for banks also lie in how to reach out to customers in an effective manner and offer them meaningful products and services. The banks are now looking at tools of digital convergence to tackle challenge from competitors as well as enhance customer satisfaction. They are now making substantial investments to leverage customer data analytics and predictive analytics for several critical tasks like fraud detection, risk management, getting customer insights and reaching out to customers using social media.

Parallely, we are also seeing the phenomenal growth of big data analytics. Big Data is characterized by the four V’s --- Volume, Velocity, Variability and Veracity. Over the years, there has been an explosion of data that is emanating from POS (point-of-sale), EDI (Electronic Data Interfaces), ATM transactions and Internet Banking transactions. As per information given by IBM, there are about 2.5 quintillion bytes (1 quintillion=1018) of data being generated every day. This data is mostly in unstructured form and is used for risk management, fraud detection, product customization and customer perceptual analysis.

This research paper will delve into the challenges faced by the Banking Industry in analyzing Big Data and utilizing the same in an effective manner for the profitability and growth of the business. The research work shall employ both primary and secondary data and analyze the same using an empirical framework to identify the critical success factors that are essential to successfully employ big data analytics to grow the banking business with minimum risks.


Banks have been the mainstay and supporting backbone for most of the industries nationally and globally since time immemorial. From the time the barter system was replaced with monetary system, banks have played a key role in treasury management, working capital management, investment management, project financing and several key activities. Today, the global banking industry does business worth $ 410 trillion every year with more than 15,000 banks taking active part in financial transactions. The leaders in the banking industry globally are Citibank, HSBC, JP Morgan, Goldman Sachs, Deutsche Bank, Wells Fargo, Bank of America, UBS, Credit Suisse and ICBC.

The banking industry in India has also been growing phenomenally since independence. A string of financial mismanagement and scandals had forced the Government to nationalize most of the banks in the 1960s and 70s. However, after 1991, following in the footsteps of liberalization, globalization and privatization, the private banking also got revived in a big manner.

As per information provided by Reserve Bank of India (RBI), there are presently 26 public sector banks, 20 private sector banks and 43 foreign banks who have been given the permission to undertake banking operations in India. Another two entities, IDFC (Infrastructure Development and Financing Corporation) and Bandhan, which was earlier a micro-finance company, have got banking licenses. There are also 61 regional rural banks and more than 90,000 co-operative banks. The banking sector in India has a net worth of Rs 81 trillion ($1.31 trillion). As per research done by KPMG and CII, the banking industry in India is all set to become the fifth largest banking industry in the world by 2020 and the third largest by 2025.



The growth of banking industry in India and across the world has been fuelled by two major strategic developments, namely, that of innovation and financial deregulation. The Riegle-Neal Act of 1994 removed interstate banking and branching restrictions in USA. The Gramm-Leach-Bliley Act of 1999 removed restrictions on mergers between commercial banks, investment banks, securities firms and insurance companies. The formation of European Union paved the way for a single currency in European nations and free flow of capital across all the member nations.

The financial deregulation in India received a major thrust with the implementation of liberalization-privatization-globalization policy by the Narsimha Rao - Manamohan Singh Government in 1991. The Narsimhan Committee set up by the Indian Government in 1991 gave key recommendations for increased autonomy of Public Sector Banks, which were later adopted by the policy makers. Subsequently, a second committee set up under the stewardship of Mr Narsimhan in 1997 advocated the healthy competition between private sector and public sector banks.

The new millennium has also seen rise in innovative approaches to banking. The technological revolution that popularized laptops and mobile phones and brought the internet closer to the masses has also been leveraged in a big manner by banks. Starting with internet banking, mobile banking and remote banking, technology has helped banks to reduce the dependence on physical branches and reach out to wider range of customers using virtual banking tools. In recent years, the mobile banking has been recording a stupendous growth with volume of business increasing from 25.56 million in 2012 to 53.3 million in 2013. The value of transactions during this period has grown from $0.2 billion in 2012 to $1.1 billion in 2013 (Source: Business Today, Jan 18, 2015).
As per a report published in Business Today, 85 percent of transactions by customers of HDFC Bank now take place through non-branch channels. The mobile and internet banking channel contributes almost 55 percent. HDFC Bank is now transforming themselves from a brick-and-mortar entity into a full scale digital bank.



But the innovation and deregulation, though contributing to the rapid growth of the banking industry, has also come at a cost. As per a report published in Business World (29 December, 2014), between January 2008 and December 2011, 414 insured US commercial banks failed. Of these, 85 percent were small banks with less than $1 billion in assets. The common underlying causes for failure were excessive credit growth and dud realty loans.

The Indian banks were also at the receiving end, seeing their NPA (Non-Performing Assets) growing to a disproportionate size. The gross NPA of Indian banks was at 4% as on 31st March, 2014. The same figure was 4.2% on 31st March, 2013. The total write-off of loans by banks in the last five years was Rs 1,61,018 Crore. Owing to increasing number of scandals in the industry and stricter policy from Reserve Bank of India, Indian banks are looking to upgrade their technology systems to analyze real time data to predict fraud or illegal activities.

There is also a competition prevailing in the banking industry over increasing the reach to customers using internet based tools. The banks are displaying customized product offerings through Internet Banking, Mobile Banking and ATM. HDFC Bank is going digital in a big manner. There is a very systematic focus within the bank in making customers use and adopt digital channels. HDFC Bank has already invested in data warehousing, analytics, outbound call centers and models for customer relationship management.

The efforts to reduce the risks in banking transactions and increasing reach to tap into wider customer base needs access to a huge quantum of data and the capability to process this data to draw out meaningful inferences that can be used for decision making. This is where Big Data Analytics is poised to play an important role in promoting the growth of the banking industry as well as mitigating the quantum of risks. Our study is an endeavor to understand how big data analytics can be used effectively by the banking industry to formulate strategies to contribute to the growth of the topline and the bottom-line of the banking entities.

The Advent of Big Data Analytics

Gartner defines big data as high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Every day, there is creation of 2.5 quintillion bytes of data. Data volume is increasing exponentially from terabytes to petabytes, exabytes and now zettabytes. According to IBM, 80% of data captured today is unstructured, from sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals, to name a few. All of this unstructured data is big data. Big Data is a collection of huge and complex data sets that becomes difficult to process using on-hand database management tools or traditional data processing applications.

FIGURE-3 : Big Data = Transactions + Interactions + Observations

Big Data =  Transactions + Interactions + Observations

Big Data is getting mainly getting generated by:

  • Social Media and Networks
  • Scientific Instruments
  • Mobile Devices
  • Sensor Technology and Networks

The payoff from using big-data analytics for analyzing transactions related to banking is enormous. The quantum of successful case studies continues to build, reinforcing broader research suggesting that when companies inject data and analytics deep into their banking operations, they can deliver effective sales and higher profit gains. The new strategy of data-driven sales, more in-depth information about consumer behavior, better predictions, and shorter decision making cycles is making companies adopt this model at a faster rate. According to Gartner, Big Data will drive $232 billion in IT spending through 2016.

Hence, there is a requirement for a strategic plan on how banks can leverage data, analytics, frontline tools, and people to create business value. The effectiveness of the plan shall lie in creating a common language allowing senior executives, technology professionals, data scientists, and marketing managers to discuss where the greatest returns will come from.

FIGURE – 4 : The Four V’s of Big Data

The Four V’s of Big Data

Research Methodology

Research Objective

A Study to analyze the effectiveness of using Big Data Analytics in Banking Industry

Research Design

The Research Design for the study is based on descriptive research model in which the analysis has been done on the basis of data collected through primary research and also research of relevant published secondary data.

Data Capturing Instrument

The data was captured using questionnaires. Each questionnaire comprised of 10 questions.

The data that was sought pertains to the challenges faced by the banks in processing big data and adopting strategies to use the same in a meaningful manner.

The questionnaires were personally administered as well as sent and collected through e-mail.


The primary data has been collected through questionnaires administered to the banking professionals who are aware of the technologies being used in the banking process and using the same in day-to-day operations and transactions.

The sample size for primary data collection was 100. The respondents were selected through simple random sampling process.

The secondary data has been collected from a diversified pool of resources such as newspapers, journals and research articles published by consulting companies like Mckinsey, E&Y, PwC, KPMG and CII. The secondary data shall help to validate the inferences drawn from the primary research.

Data Analysis

The data has been analyzed using statistical tools like graphical analysis and percentage analysis.

Results / Findings

1.Do you use big data analytics for your banking operations?

2.What are the main purposes for which you use big data analytics?

3.How well trained are your personnel to leverage the power of big data analytics?

4.What are the main challenges that you face in utilization of big data analytics?

5.What is the importance of big data analytics for formulating strategies in your banking operations?

6.What benefits are you deriving by using big data analytics?

7.Has big data analytics contributed significantly to the topline and bottom-line of your business?

8.Has big data analytics been effective to provide meaningful insights into customer behaviour?

9.What do you think will be the future drivers of banking operations?

10.What are your recommendations for better utilization of big data analytics?

Data Inference and Conclusion

The Big Data Analytics is mainly used by Banks for Marketing Analytics, Risk and Fraud Management, and Strategy Formulation. Big data analytics help banks to do customer segmentation, customer retention, campaign management, cross-selling and up-selling activities. In risk and fraud analytics, banks use credit scorecards to estimate propensity to default and build early warning system (EWS) to get alerts on fraudulent practices. For strategy formulation, banks use data visualization interfaces and profit and loss (P&L) reporting.

FIGURE – 5 : Application of Big Data Analytics in Banking

Application of Big Data Analytics in Banking

Banks like HDFC and ICICI Banks had started investing in big data analytics since 2004. They built up data warehouses and also invested in cutting edge data mining tools that could unravel business trends, map customer preferences and provide alerts to prevent risky occurrences. The benefits that they have accrued are now motivating other bankers to put in a system to capture and analyze big data. Here, based on our study, we would like to propose the following strategies that would help banks to employ big data analytics in an effective manner.

Use of Predictive Analytics

Predictive Analytics can help the banks to decide on the right mix of customers as well as decide on interest rates and insurance premiums. Predictive Analytics can also help banks to cross-sell and up-sell banking products. The other applications of predictive analytics are in risk management, fraud management and marketing campaign management.

Predictive Analytics play a key role in helping banks to retain the customers. The cost of acquiring a new customer is much higher than retaining the old customers. Predictive Analytics make it much easier to identify the dissatisfaction issues pertaining to a customer and rectifying them well in advance to keep the customer loyal to the bank. Predictive tools like SAS text miner, IBM SPSS, COGNOS and SAP-Hana have the capabilities to mine the data and draw out predictive inferences for bank to act upon.

Leveraging internal, external and social media data

Big Data comprises of information drawn from the bank’s internal interfaces, external interactions and the data emanating from social networks. The internal data comprises of the banks financial records like balance sheet, profit and loss statement, and cash flow – fund flow statements. The external data gets created during e-mail exchanges, telephonic banking operations, internet banking and mobile banking transactions and during ATM usage. The social media data comes from Facebook, Twitter and LinkedIn discussions and Google search engine operations. The banks need to harness and synchronize the internal, external and social media by doing multivariate analysis to get meaningful insights.

The Netherlands based Rabobank has been a pioneer of using big data analytics. They began their initiative with analyzing the internal data. Then, they went for full-fledged big data analytics by incorporating analysis of public data from government sources, click-behaviour data and social network data. They also built small clusters using open source technology to test and analyze unstructured data sets. Rabobank now extensively uses big data analytics to decide on the locations of ATMs which can offer them better leverage.

Creating Master Data Management Framework to manage structured and unstructured data

Big Data comes in both structured and unstructured formats. The structured data is in the form of quantifiable numbers while the unstructured data is mostly qualitative in nature in the form of text, images or audio and video recordings. While historically banks have till now depended on structured data to analyze, draw inferences and take decisions, the changing scenario makes it imperative that they pay equal or even more attention to unstructured data which can give them even more incisive inferences. To achieve this banks have to create a master data management framework that can manage both structured and unstructured data.

Bank of America is driving several initiatives to manage structured and unstructured data and reorganizing their internal operations to understand customers in a better way and offer them customized products and services that would have more appeal. One of their initiatives is called “BankAmeriDeals” that provides cash-back offers to their credit card and debit card holders based on the payment records these customers. They also offer refinancing deals to their customers after analyzing data on credit card usage or mortgage loan repayments. BoA uses a dedicated Grid-Computing platform to detect the high-risk accounts and take corrective actions to prevent defaults.

Training the Employees

The adoption of big data analytics can be successful only if there is an internal thrust and agreement from inside the organization that is both top driven and bottom driven. To achieve a completely successful implementation, big data analytics should not be restricted within the precincts of the IT or MIS departments. It is only when each and every employee is well trained on big data analytics that they will understand the importance and the returns that can be generated by the adoption of new age tools.

To make the big data analytics implementation program more successful, Bank of America created a matrix organizational structure wherein the analytics teams were interspersed with other functionaries. This helped every unit of the bank to analyze customer data, understand them in a better manner and offer customized services to gain more customer satisfaction.

Educating the Customers

The customers are the end users and the final beneficiaries of big data analytics. For success of big data analytics, the customers have to be taken into confidence and educated about the way the banks are adopting new age technologies to offer them real time, prompt and effective services that leads to higher degree to customer satisfaction. That will ultimately get translated into higher degree of customer loyalty leading to better margins and ROI (return on investments).


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