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Big Data in Banking – Leapfrogging into Digital Banking Era

Big Data in Banking – Has it occurred to you that banking and financial services are becoming increasingly more customer-oriented and customer-friendly?

Is it possible to get a “Buy one get one free” offer on BookMyShow tickets if a bank asks you to use your credit card in exchange for the offer? If you use a specific bank’s credit card service and receive discounts on flight tickets or hotel reservations, that’s a win-win situation.

You might wonder how these financial institutions come up with such enticing offers and draw you in with their latest banking services and products.

You’ve probably noticed that, the moment you step into the world of business, you’re bombarded with hundreds of offers for credit cards, home loans, car loans, and a slew of other products.

What strategies do these financial institutions use to entice you to use their services and ultimately convert you into one of their most valuable customers?

In today’s world, banking and other financial institutions are heavily reliant on Big Data Analytics to acquire new customers, increase profitability, sell cross-sell and up-sell products to existing customers, detect fraud, and streamline the entire banking transaction process.

According to the Research and Markets Report, the value of big data analytics in the banking industry in 2017 was US$ 7.19 billion. Over the forecast period of 2018-2023, it is expected to grow at a compound annual growth rate (CAGR) of 12.97 percent to reach US$ 14.83 million by 2023.

Big Data in Banking Industry

Nowadays, data is extremely important in the BFSI sector, which is replete with data. Big decisions, whether they are related to policymaking, financial statement analysis, banking rules and regulations, or other areas, are based on information gathered from data.

We obtain the information for our analyses from a variety of sources, some of which are listed below:

  • Personal Information about the customer
  • Information about the account
  • Transactions with Customers
  • Customer complaints and service inquiries are handled by a dedicated team.
  • Market sentiment, product performance, and other information are fed into social media.

Major business challenges such as profitability, performance, and risk accessibility are being addressed by banks through the use of Big Data Analytics. The technology is also assisting financial institutions in lowering the cost of customer acquisition, predicting mortgage default risk, and most importantly, identifying genuine customers.

3 V’s of Big Data

Let’s take a look at how the three V’s of big data can be applied in the banking sector:

1. Variety: Different data types are required to store different types of information. In the course of their business, banks generate various types of data, including customer information, transactional information, financial statements, credit scores, loan information, and so on.

2. Velocity: A measure of how quickly new data is added to the bank’s database is related to this measure. SBI receives an additional 4 TB of banking data every day, and its data warehouse contains more than 120 TB of information.

3. Volume: It refers to the amount of storage space required to store this information. Several terabytes of data are generated every day by large financial institutions such as the Bombay Stock Exchange (BSE).

Using Big Data Analytics in the Banking Industry has a lot of possibilities.

1. Preventing Frauds

It was discussed in the HDFC case study that big data analytics can be used to significantly reduce the incidence of fraudulent activities.

2. Identifying and Acquiring Customers

Customer acquisition is more expensive for banks than retaining existing customers. A variety of services, such as purchase discounts, home buying made simpler, personalized services and information, as well as alerts and notifications, may be required by customers at different times.

Traditional data processing tools are insufficient for all types of decision-making because they cannot process all types of data. As a result, banks are effectively employing data analytics to increase customer value while also making better and faster decisions.

3. Retaining Customers

With technological advancement, there is less interaction between customers and bankers, at least in terms of ensuring that the current customer is satisfied with their services in order to keep them as a customer.

4. Enhancing Customer Experience

We have seen in the First Tennessee Bank case study how big data analytics can be used to improve the customer experience and thus increase revenue.

5. Optimizing Operations

Big data analytics can be used to make decisions about the location of branches and ATMs. It is the desire of banks to open a branch in order to serve a greater number of customers. The establishment of a bank branch in a prominent location has the potential to significantly increase the customer base.

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

Fiscal and monetary policies are frequently changed in the banking and financial services sector as a result of this. Using big data analytics, it is possible to make dynamic decisions based on the most recent policies. Using big data analytics, it is simple to compare and contrast different predictions made with different inputs.

7. Improving product design/Optimizing overall product portfolio

Banks can create a variety of products based on the demographics and banking habits of their customers. Using Big Data Analytics, it is possible to forecast the profitability of products based on the estimated number of customers. With the help of Big Data Analytics, we can also forecast product demand.

8. Increasing Transparency

Fraudulent activities and suspicious accounts should be closely monitored in order to improve the overall transparency of the banking system. Big data analytics will assist in keeping an eye on all of these malicious activities, and will alert the appropriate authorities as a result.

Examples of Big Data in the Financial Services Industry from real-world situations

1. First Tennessee Bank

Multiple lucrative offers targeted at a high-value customer segment resulted in a 600 percent return on investment.

Marketing expenses were reduced by nearly 20%.

Predictive analytics assists in better understanding the customer and their spending habits. According to the needs of the customers, this facilitates the process of cross selling and up selling.

Consequently, banks can now create customized sales strategies for their target customers, resulting in a significant increase in their overall revenue stream.

For example, First Tennessee Bank used predictive analytics to improve the effectiveness of its market strategy.. In addition to increasing the customer response rate by 3.1 percent, the highly targeted campaigns helped to reduce marketing costs by nearly 20 percent.

2. ICICI Bank Case Study – Credit Risk

The application of analytics has resulted in several process efficiencies, including a reduction in manpower requirements by 80 percent.

Debt collection has been identified as the most important process for increasing customer satisfaction. In order to transform debt collection into a customer retention tool, the appropriate customer-approach channel must be used.

When it comes to debt collection, ICICI Bank employs a variety of methods. The bank has implemented a “centralized debtors allocation model,” according to which appropriate debt collection channels are assigned to each overdue case in order to maximize collection efficiency.

The processes have become significantly more efficient as a result of the use of analytics. In addition, the number of employees has been reduced by 80 percent.

3. SBI Case Study

SBI has hired a large number of Data Analytics professionals in recent months to develop a variety of analytical data models for use in the following areas:

  • Automate the loan disbursement process at the company (Automation of education loans, Car loans, Home loans, etc.)
  • Increase the level of transparency in the process of providing loans to customers.
  • Increasing the efficiency of the loan disbursement process
  • Increasing the number of nonperforming assets is a priority.

Aspects of analytics will also assist in determining the most advantageous location and cash limit for each of the ATMs.

4. HDFC Case Study

The analytics tool provides a better understanding of the personal habits of its customers, which allows it to promote offers more effectively.

A significant reduction in money laundering can be achieved through the use of Big Data Analytics/Hadoop. It can aid in the identification of questionable activities such as:

  • Cash deposits made in large quantities on a single day
  • Money is being transferred to multiple accounts.
  • Opening a large number of accounts in a short period of time
  • Accounts that have been dormant for a long time suddenly become active.
  • International transfers with a high volume

Conclusion

Every day, hundreds of thousands of transactions take place in the banking industry. Transactional data must be properly evaluated, scrutinized, and leveraged for the benefit of banks and their customers in order for them to be successful.

Technologies such as Hadoop and Big Data Analytics are extremely useful in gaining valuable business insights that can be used to increase customer satisfaction and loyalty.