The Good Tech Companies - How Big Data is Reshaping Customer Experience in Banking

Episode Date: January 5, 2026

This story was originally published on HackerNoon at: https://hackernoon.com/how-big-data-is-reshaping-customer-experience-in-banking. Big data analytics helps banks personalize services, predict customer behavior, reduce churn, and improve satisfaction through AI-driven insights. Check more stories related to finance at: https://hackernoon.com/c/finance. You can also check exclusive content about #banking-data-analytics, #ai-personalization-in-finance, #predictive-analytics-banking, #big-data-in-banking, #customer-churn-reduction, #customer-experience-banking, #ml-in-banking, #good-company, and more. This story was written by: @jonstojanjournalist. Learn more about this writer by checking @jonstojanjournalist's about page, and for more stories, please visit hackernoon.com. Big data is transforming banking by enabling deep customer insight and hyper-personalized services. By analyzing transaction histories, demographics, and digital behavior with machine learning, banks can predict churn, improve engagement, and tailor offerings. Research shows personalization boosts satisfaction, reduces churn by half, and is becoming essential for competitive, customer-centric banking.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. How Big Data is reshaping customer experience in banking by John Stoy and journalist. In today's rapidly evolving financial landscape, big data analytics has become more than just a buzzword. It's a strategic asset. To understand how data is revolutionizing customer engagement in banking, we sat down with Ahmed Tenaja, author of the recent study, customer behavior analysis in banking, leveraging big data to enhance personalized services, published in the International Journal of Innovative Research and Science, Engineering and Technology Q. Can you summarize the main idea

behind your research? Amit? Absolutely. The core of the study revolves around how banks are harnessing big data analytics to understand and predict customer behavior more accurately than ever before. We're talking about leveraging large-scale data sets like transaction histories, demographic data, and digital activity logs to provide highly personalized financial services. Q. That sounds powerful. What kind of customer data are banks analyzing? Amid. Banks typically analyze three major types of data. One. Transaction histories. This includes data on frequency, volume, and type of financial transactions. Two, demographic data, age, income, gender, occupation, and location all help segment customers.

3. Online activity logs. How often a user logs in, what services they interact with, and how long they spend on specific platforms are all monitored to gauge preferences and engagement. Q. How do banks make sense of such large and varied data? Amit? Great question. That's where machine learning comes in. Two key techniques we focus on in the paper are clustering, unsupervised learning. This group's customers with similar behaviors. For example, we can identify patterns in spending and segment. customers as transactors, revolvers, or inactive, users. Segmentation, supervised learning. This uses decision trees are K-nearest neighbor algorithms to classify customers based on known outcomes or characteristics. Q.

What data sets were used in your analysis? Ahmed. We primarily used, the bank marketing dataset from the UCI Machine Learning Repository, which includes real campaign and demographic data from a Portuguese bank. The Kaggle customer segmentation data set. which, while anonymized, is great for modeling realistic banking behavior using demographic and transaction data. Q.

Any predictive modeling insights you'd like to share omit, definitely. We built several predictive models to forecast outcomes like customer churn or campaign response rates. For example, using the bank marketing dataset, random forest algorithms performed best with 91% accuracy, 93% precision, 90% recall. This outperformed other models like logistic regression and decision trees. Q. Did this approach translate into tangible improvements for banks?

Ahmed, yes, and the results were impressive. Here are two concrete outcomes. Customer segmentation success. For instance, banks could tailor offerings by understanding segments like high transaction users versus low engagement ones. Personalized recommendations. These led to increased customer satisfaction. In fact, that satisfaction

scores rose by about one full point, on a five-point scale, after implementing personalized recommendations. Q. And what about customer loyalty and churn? Amit, big data-driven personalization has been shown to reduce churn dramatically. Churn rates from 2019 to 2021 shows a consistent drop from 12% to 6% when banks shifted from traditional marketing to big data-driven strategies. Q. So, what's the takeaway for banking leaders? Amit. Big data and analytics isn't just a competitive advantage, it's quickly becoming a necessity. By anticipating customer needs, personalizing services, and proactively reducing churn, banks can stay relevant and build lasting customer relationships in a fast-changing digital landscape. Q. Last question,

what's next for this field? Amit. The next frontier is integrating real-time analytics and eye-driven personalization. Imagine a bank that not only knows your spending habits but adapts its offerings to your lifestyle in real time. That's where we're headed. Thank you for listening to this hackernoon story, read by artificial intelligence. Visit hackernoon.com to read, write, learn and publish.