In today’s data-driven world, data analytics in banking play a crucial role in informed decision making to drive organizations forward, improve efficiency, increase returns, and in turn achieve business goals. For the uninitiated, data analytics is the process of discovery, interpretation, and conveying meaningful insights from the data to help in the decision-making process……

According to the latest Worldwide Semi-Annual Big Data and Analytics Spending Guide from one of the top research firms, worldwide revenues for big data and business analytics will go up to more than $203 billion in 2020. The applications for data analytics are significantly growing day by day because of various innovations in the field. Out of this $130 billion market share, the banking sector leads revenues with a contribution of $17 billion in 2016.

In the Banking and Financial Services sector, through data analytics, institutions can monitor and assess large amounts of customer data and create personalized/customized products and services specific to individual consumers.

For example, when a customer buys a vehicle, the bank sends promotional offers of insurance to cover the customer’s vehicle. In the future, such applications could be expanded even further. One way this could happen is if a customer got a large bill, the bank could offer an EMI conversion or a loan to cover the cost.

Some of the areas where banking and financial institutions are increasingly using data analytics include:

  • Fraud detection
  • Managing customer data
  • Risk modelling for investment banks
  • Personalized marketing
  • Lifetime value prediction
  • Real-time and predictive analytics
  • Customer segmentation
  • Customer spending patterns
  • Transaction channel identification
  • Customer feedback analysis and application

The importance of data analytics in the banking and financial services sector has been realized at a greater scale and most of the established banks have already started reaping the benefits.

For instance, an American bank used machine learning to comprehend the discounts that its private bankers were providing to customers. Bankers were claiming that they offered discounts only to important/ valuable customers. However, when the data was assessed through analytics, it showed a different story. It showed the discount patterns which were not needed, and which could easily be corrected. The bank adopted the changes, leading to an increase in revenues by 8% within few months.

A leading industry survey conducted for 20 banks across the EMEA region revealed that there were certain areas of improvement, which if worked upon could deliver great returns. Some of the areas included were:

  • Aligning the priorities of analytics to the strategic vision of the banks
  • Incorporating decision making with analytics practices
  • Developing advanced-analytics assets on a large scale and investing in the roles which are critical to analytics
  • Enabling the user revolution with clearly defined data ownership and maintenance of high-quality data

To gain competitive advantage, banks should recognize the importance of data science, incorporate it in their decision-making process, and develop strategies based on the actionable insights from their customers data. Start with small, doable steps to integrate data analytics into operating models and stay ahead of competition.

data analytics in banking