How Important is Analytics in Banking Sector?


How Important is Analytics in Banking Sector?

Analytics can help banking organisations in differentiating themselves and build a competitive edge. For every financial institution, data analytics has been a vital aspect, starting from investment banking and credit banking to securities trading and more. Over the last decade,big data analytics has opened so many potential opportunities for banks to grow and stay competent.

With the extensive use of advanced analytics, banks must understand the components that make up the technology. Advanced analytics can be classified into four major categories.

  • Reporting: It mainly focuses on the conversion of raw data into information, building data repositories with the help of basic analytics, such as reporting suspicious activities.
  • Descriptive Analytics: The phenomenon of processing, identifying patterns, and summarizing the information gathered in reporting, such as customer segmentation based on spending behavior.
  • Predictive Analytics:  This uses the patterns mentioned above to predict future actions or other scenarios, such as personalization of customer offerings based on the segmentation.
  • Prescriptive Analytics: It collects results from descriptive and predictive analytics to determine the scenarios like what, why and how will happen.

The above mentioned components will drive advanced analytics, which will help the users to search, conduct, and analyse forecasts and predictions. Analytics also helps banks improve decision making and optimize everyday activities to enhance productivity. As of now, advanced analytics has been tremendously improving customer experience.

Advanced Analytics in the banking sector also plays a key role in detecting fraud. Using transaction monitoring systems, fraudulent activity can be detected, but this requires manual intervention and is time-consuming. Similarly, banks are adopting advanced analytics to get more customers via target optimization, i.e., developing deeper customer segmentation to identify the right targets on the right channel.

For banks to increase the lifetime value of a customer, they have to analyse and work on the customer retention strategies. This includes quality of service (QoS), identifying at-risk customers, and more. Analytics can help banks to move deeper into customer services and identify behaviour patterns and paths. Banks must evolve accordingly and value the rapid changes in the data analytics field and inculcate into the already existing environment which fights for the existence in the digital world.