Big Data in Banking – Leapfrogging into Digital Banking Era

Information is the oil of the 21st century, and analytics is the combustion engine.” – By Peter Sondergaard

Did you notice that banking and financial services these days are becoming more and more customer-oriented and customer-friendly?

What if a bank asks you to take a credit card and, in turn, get a “Buy one get one free” offer on a BookMyShow ticket. How about if you avail the credit card service of a particular bank and get discounts on flight tickets or hotel bookings.

Have you ever thought about how these banks come with such fascinating offers and attract you with their latest banking products?

Did you notice, as you enter into corporate life, you get hundreds of offers regarding credit cards, home loans, car loans, and various insurance products, etc?

How do these banks lure you to opt for their services and finally try to make you their key customers?

Banking and other financial institutions these days are highly leveraging on Big Data Analytics to acquire new customers, increasing profitability, cross-sell/up-sell products to its customers, detect frauds, and streamline the complete banking process.

Big data analytics in the banking industry was valued at US$ 7.19 billion in 2017, according to the Research and Markets Report. It is expected to grow at a CAGR of 12.97% during the period 2018-2023 to reach US$ 14.83 million by 2023.

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Big Data in Banking Industry

Nowadays, data plays a very important role in the data-rich BFSI sector. Major imperative decisions be it related to policymaking, financial statement analysis, banking rules, and regulations, etc. are data-driven.

We get the data for analysis from various sources, some are mentioned below:

  • Customer Personal Details
  • Account Details
  • Customer Transactions
  • Customer complaints and service inquiries
  • Social Media Feeds
  • Market Sentiment
  • Product Performance, etc.

Banks are overcoming major business challenges like profitability, performance, and risk accessibility through Big Data Analytics. It is also helping the banks to reduce the cost of customer acquisition, predicting mortgage default risk, more importantly, identifying the authentic customers.

3 V’s of Big Data

Now let us see how 3 V’s of big data can be applied in the banking industry:

1. Variety: To store different types of information, we need various data types. Banks generate various types of data, be it related to customer information, transactional information, financial statements, credit scores, loan details, etc.

2. Velocity: It is related to the speed with which new data is added to the bank’s database. SBI receives an additional 4 TB of banking data daily also it’s data warehouse has over 120 TB of data.

3. Volume: It is the amount of space required to store this data. Huge financial institutions like Bombay Stock Exchange (BSE) generate terabytes of data daily.

Potential of Big Data Analytics in the Banking Industry

big data in banking

1. Preventing Frauds

As discussed in the HDFC case study, fraudulent activities can be controlled significantly through big data analytics.

2. Identifying and Acquiring Customers

For banks, customer acquisition is more costly than retaining old ones. Customers may be requiring varied services such as purchase discounts, simplified home buying, personalized services, information, and alerts, etc.

The traditional tools are not sufficient to process the data for all types of decision making. Hence, banks are efficiently using data analytics to enhance customer value, along with better and faster decisions.

3. Retaining Customers

Due to technological advancement, there is not much interaction between customers and bankers at least to ensure that the current customer is well satisfied with their services to retain them.

4. Enhancing Customer Experience

From the First Tennessee bank case study, we have seen how big data analytics help in improving customer experience.

5. Optimizing Operations

Big data analytics can make decisions related to Branch and ATM locations. Banks would like to open a branch where they can cater to more customers. Opening a bank branch in the prime location can significantly increase the customer base.

6. Meeting Regulatory Requirements/ Addressing setbacks on a real-time basis

In the banking and financial services sector, fiscal and monetary policies change frequently. Big data Analytics can make dynamic decisions based on the latest policies. Analyzing various predictions by altering inputs can be easily done through big data analytics.

7. Improving product design/Optimizing overall product portfolio

Based on customer demographics and banking habits, banks can design various products. Big Data Analytics can help predict the profitability of the products based on the estimated customers. We can also predict product demand through Big Data Analytics.

8. Increasing Transparency

Keeping a strict check on fraudulent activities and suspicious accounts will increase the transparency in the banking system. Big data analytics will help keep an eye on all these malicious activities, thereby alerting the authorities.

Case Studies related to Big Data in the Banking Industry

1. First Tennessee Bank

Various Lucrative offers targeted to the segment of high-value customers resulted in a 600% ROI

Cutting marketing costs by nearly 20 percent.

Predictive analytics helps in understanding the customer more precisely and also helps in understanding their spending behavior. This, in turn, helps in Cross-selling/Up-selling the products according to the customer needs.

Banks can now design customized sales strategies for the target customers, thus increasing the revenue stream significantly.

E.g., First Tennessee Bank optimized its market strategy with the help of predictive analytics. The highly-targeted campaigns helped increase customer response rate by 3.1 percent and cut marketing costs by nearly 20 percent.

2. ICICI Bank Case Study – Credit Risk

The use of analytics has brought several efficiencies in the process, such as reducing manpower needs by 80%.

Debt Collection is identified as the key process to improve customer satisfaction. The right customer-approach channel is required to transform debt collection as a customer retention tool.

ICICI bank uses multiple channels for debt collection. The bank has used a “centralized debtors allocation model” through which appropriate debt collection channels are allocated to each overdue case.

Through analytics, efficiencies of the processes have increased significantly. Also, there is an 80% reduction in manpower.

3. SBI Case Study

SBI has hired many Data Analytics professionals in recent months to generate various analytical data models to:

  • Automate its loan disbursement process (Automation of education loans, Car loans, Home loans, etc.)
  • Bring transparency in giving loans to the customers
  • Speeding up the loan disbursement process
  • Reducing the number of nonperforming assets.

Analytics will also help in deciding the optimal location and cash limit for each of ATMs.

4. HDFC Case Study

The analytics tool gives an understanding of the personal habits of its customers to promote offers.

With the help of Big Data Analytics/Hadoop, money laundering can be reduced significantly. It can help identify doubtful activities like:

  • Single-day cash deposits in large volumes,
  • Transferring money to multiple accounts,
  • Opening several accounts in a short period
  • Sudden activity in long-dormant accounts.
  • High Volume international transfers


Millions of transactions are happening daily in the banking industry. This transactional data needs to be properly evaluated, scrutinized, and leveraged for the benefit of the banks and their customers.

Technologies like Hadoop and Big Data Analytics come in handy to draw important business insights to increase customer satisfaction and loyalty.

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