Data Analytics Applications in Banking Customer Service and Financial Operations Management
This research proposes a data analytics framework that integrates transaction analysis, service monitoring, and performance reporting to enhance customer service and operational decision-making in banking, demonstrating its effectiveness through a synthetic dataset due to the unavailability of proprietary data.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the modern banking world, every action leaves a digital footprint. When a customer deposits a check, transfers money, or calls to ask about a balance, the bank records the event. For decades, these records sat in separate piles: one stack for money moving in and out, another for customer complaints and questions, and a third for how long it took to solve problems. While banks have long known that looking at these numbers helps them run better, a persistent challenge has been connecting the dots between what customers spend and how they are treated. The question facing financial institutions is not just whether they have data, but how to weave these different threads into a single, clear picture that helps managers make better decisions in real time.
A researcher at Lamar University, Sadia Afroje, tackled this challenge by designing a new way to look at banking operations. The goal was to build a system that does not just store information but actively connects transaction history with customer service logs. In the real world, banks often struggle to see how a spike in transaction volume might relate to a rise in customer calls, or how long it takes to resolve a specific type of complaint. This study proposes a framework that brings these separate streams of information together. By combining data on financial transactions with records of service requests, the system creates a unified view of how a bank is performing. It is important to note that because actual bank records are private and protected, the researcher did not use real customer data. Instead, she created a realistic, made-up dataset that mimics the behavior of a typical bank over six months to test if her new system would work.
The framework operates like a three-step process. First, it gathers the raw numbers, such as the time a transaction happened, the amount of money involved, and the moment a customer called for help. Second, it cleans and organizes this information, ensuring that a transaction and a service call from the same person can be linked together. Finally, it turns these organized facts into a visual dashboard that managers can read at a glance. This dashboard acts as a control panel, displaying key indicators like how long it takes to answer a customer, how many requests are solved on time, and the average size of transactions. The system calculates these metrics by simply counting and averaging the data, looking for patterns that might otherwise remain hidden in spreadsheets.
When the researcher ran this system on the synthetic data, the results showed that the approach could successfully track performance over time. The simulated dashboard revealed that the average time to respond to a customer request fluctuated between 13.0 and 16.8 minutes over the six-month period. It also showed that the bank was solving more than 80 percent of service requests efficiently throughout the test. The system tracked the average value of transactions, which rose from 520 dollars in January to 605 dollars in June, while simultaneously monitoring the volume of service requests, which varied month by month. These findings suggest that when transaction data and service data are viewed together, managers can spot trends more easily, such as whether a busy month for spending is also a busy month for customer support.
The study highlights that this integrated view offers a significant advantage over looking at data sources in isolation. By seeing the relationship between how much money moves and how customers interact with the bank, institutions can better understand their operational health. For instance, the system could identify that certain types of inquiries take longer to resolve than others, or that customer satisfaction scores tend to drop when response times stretch too long. The research demonstrates that such a structured approach allows banks to move from guessing about their performance to knowing exactly where they stand. While the results come from a simulated environment rather than a live bank, the study argues that the method is sound and ready to be tested with real-world data.
The author acknowledges that this work has limits. The current model relies on clean, structured numbers and does not yet account for the messy, unstructured feedback customers might leave in open-ended comments. It also focuses on quantitative measures, such as time and money, rather than the deeper feelings or perceptions of the customers themselves. However, the study provides a clear blueprint for how financial institutions can organize their data to improve service. It suggests that by building a system that connects the dots between what customers do with their money and how they are helped when things go wrong, banks can make smarter, faster decisions. The path forward involves testing this framework with actual bank data and expanding it to include more complex types of information, but the core idea—that connecting these two worlds creates a clearer picture of performance—stands as a practical step toward better banking.
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