According to World Retail banking Report 2022, Retail banks are lagging in the ability of providing omnichannel experience which has led to the customers twirling towards the competitors providing more customized experience. In a survey conducted, more than 75% users are using cost-effective and flawless services provided by FinTechs.
This has created a need for banks to strengthen their data and AI to customize the experience, enhance strong connections and improve customer value. Big data analytics comes into focus for providing an accelerating growth for the banking sector.
Presently, there is a substantial adoption of big data analytics in diverse banking sectors. Employing big data analytics has allowed banks to interpret customer’s expense patterns, customer’s customization, identifying additional possibilities for advertising other financial products and analyzing big amounts of data from different sources in various forms periodically.
How is big data analytics a boon for the banking sector?
Banking sector is mostly driven by data where big data analytics allow banks to penetrate into the spaces of an individuals’ life and get the insights of their way of life, preferences and their desires. Gone are the days when people used to stand in the line to deposit their paychecks in their accounts. Now, they can check their bank balance, pay bills or send money via phones.
Most of the activities are now carried, certain offline services that banks had are no longer proven to be relevant. This has made Big data analytics strategy in banking crucial. Banks can establish a swirling eye on the customers which allows them to track the purchasing patterns, segmenting customers according to their profile, execute risk management, offering customized products, employing strategies for customer retention and receive, analyze and acknowledge the customers’ feedback.
Top 5 benefits of Big data analytics in banking:
Big data analytics has revolutionized the banking experience not only for banks but also for customers. Using strategies driven by data analytics have allowed banks to unfold the possibilities of big data and helped banking businesses to quantify their gains.
- 360 degree view of customers’ activities
Customer segmentation is a commonplace for many financial services which allows them to categorize their customers based on demographics. But this does not provide information at a granular level and to understand what are the wants and requirements of their customers, these financial institutions need to use big data analytics to enhance the scope of customer segmentation by creating detailed profiles. These profiles include various factors like: customers’ demographics, products they are currently using, products they wish to purchase, offers declined by them, preferences, behavioural patterns, and so on.
- Recognizing opportunities for up-selling and cross-selling
Businesses’ first targets for selling are their existing customers and then the new prospects. Existing customers are the best options for up-selling or cross-selling the products. Big data analytics helps in understanding overall behavioural patterns of the customers which assist in identifying the potential opportunities for up-sales or cross-sales.
- Avoiding risks of Fraudulency and data managing
Banks can leverage their datatech analytics and can identify any unusual behavioural patterns to identify any scam, increasing the sense of security among customers. Big data analytics helps in managing data that constrain reporting of such unusual behaviour, auditing it and verifying. Using big data to study past transactions banks can also identify any forthcoming menances and frauds.
- Enhancing the personalized customer experience
Personalized services play an important role in retention of the customers. Big data allows banks to study the customers’ historical transactions and preferences which help banks to offer customers various personalized services that are provided.
- Receive feedbacks, analyze and respond
Feedbacks in any business are very important for identifying and improving any ambiguity in the system. Datatech analytics serves the purpose of interpreting the customers’ practices and responses. Analysis of these feedbacks helps in identifying the area for growth.
