Data Analytics in Banking: Application, Challenges & Opportunities

Ashish M. Shaji

19 Jul, 2020

Digital revolution and the advancement of technology have caused a number of industries to evolve, and the banking industry is no different.  The banking industry has been positively impacted by the digital revolution, which has caused a significant growth in Artificial Intelligence and analytics. In this article, we shall discuss the significance of data analytics in banking.

What is Data Analytics?

Data Analytics is a method of analyzing raw data that helps you to draw a conclusion about that information. Data analytics can help to reveal trends and metrics that otherwise would be lost in the mass of information. The information then can be used to optimize processes so that the overall efficiency of a business/system can be increased.

What is the need for data analytics in the banking industry?

As more and more banks are opting for digital transformation in Banking & becoming branchless, they are also looking to gain a competitive advantage to stay relevant. Therefore the need for managing big data and analytics is increasingly essential. It has caused a massive transformation in the way banks work and has helped in making informed decisions.

The significance of data analytics in banking can be assessed by the fact that it provides greater visibility into customer’s behavior and determines the probability of risk. It is noteworthy to mention here that the data collected in banks is so complex that it is almost impossible for traditional data tools to manage it. The analytics tool provides a solution to this dilemma.

Its increased efficiency and accuracy have made the banks realize the importance of data analytics, and the banks are slowly but steadily adopting it. Banks have to deal with massive amounts of data; this challenge can be turned into a new possibility by analytic tools to learn more about their clients and drive in more revenue opportunities.

Use of Data Analytics in Banking

The application of data analytics can be categorized into the following:

• Customer Segmentation

Depending upon a customer’s historical data on a customer’s spending behavior, banks can segment the customers as per their income, expenditure, etc. By understanding the profitability of specific groups of customers, banks can analyze each group and get in-depth insights.

• Fraud Management and Prevention

When banks have knowledge of the usual spending patterns of a person, it can help in identifying any suspicious activity. In case where there is a sudden increase in the expenses of a cautious customer, it clearly indicates that the card may be stolen and is being used by fraudsters. Analyzing these forms of transactions helps to minimize fraudulent actions.

• Risk Modelling

The Risk assessment is essential for banks as it helps in the regulation of financial activities and in the pricing of financial investments. The financial stability of a company can be analyzed for corporate financing and for investment purposes. Similarly, screening an applicant for a loan by knowing their spending patterns and previous credit history can help to assess the risk of issuing a loan.

• Identifying the main channels of transactions

Banks can track the usage patterns and the daily coordination between payments at their branches and ATMs. It leads to optimal management of the liquid assets, which can result in extra income. It can also help in obtaining an overview of future changes in investment and liquidity options.

• Customer Lifetime Value

Customer’s lifetime value means how long the organizations are able to retain their customers. Identifying their customers, making them better in various ways, and securing their loyalty are some of the areas where banks are focusing.

• Feedback Management

As you may know, feedback management is critical. Predictive analytics allows banks and financial firms to keep up their relationship with customers by providing them with the right services and products for meeting their needs and matching individual preferences in a sorted way.

Challenges and Opportunities with Data Analytics in Banking

Banks have understood that big data technologies will assist them in performing better and will help to strengthen their defenses against high-tech attackers. Many banks have been adopting emerging technologies, while many are still in the experimental stage.

Adapting data analytics remains a matter of choice, but issues of customer privacy have also been a concern. Rising security breaches has caused banks to apply a new approach and robust defense mechanism on a larger scale than the present ones. The greatest challenge in applying big data technologies is that the smallest oversight can lead to a loss of massive amounts of customer data. This can have adverse effects. Any security breach has the capability to cause an enormous loss to banks, which doesn’t impact them only financially, but it also affects their reputation and customer relationship.

Data and analytics tools must be used with due diligence and efficacy by the appropriate team of information security professionals. Considering the increase in fraudulent and cyber crimes, big data and analytics must be taken as a compulsion than an option for banks.

Conclusion