VISUALIZING TELLER CASH INFLOW AND OUTFLOW: A TABLEAU PROJECT FOR BANKING OPERATIONS
- Abirami Ramasubramanian
- Jan 14
- 5 min read
My Tableau Journey: Visualizing Banking Data
I started this blogathon series as the one without an IT background, curious about data and how it drives decisions in real world banking operations.
My learning journey has been step-by-step and now it culminates in exploring Tableau for data visualization
This final blog brings my skills together, focusing on visualizing the teller cash inflow, cash outflow and showing insights in a clear interactive way. The goal is not complex, but practical, actionable visualization that highlights patterns and trends in bank operations.
Turning Raw Data into Actionable Insights with Tableau
While Excel and SQL are excellent for calculating and querying data, true data engineering and visualization require a different mindset. Stakeholders, including managers, auditors, and operations teams, rarely want to go through the raw data tables. They need insights at a glance, actionable patterns, and clear comparisons.
Tableau enables this by allowing me to:
Transform transactional data into interactive visualizations that update dynamically as filters are applied.
Compare teller performance efficiently, spotting high-volume or unusual activity instantly.
Highlight trends and anomalies across multiple metrics without complex scripting or manual calculations.
For this project, the focus was on engineering clarity into the visualization pipeline, creating a precise bar chart showing cash inflow and outflow per teller over the month, making operational insights accessible in real time.
Exploring the Teller Transactions Dataset
For this project, I worked with a dataset which has one-month transaction of bank tellers, which provided the foundation for the Tableau visualization. Each record contains:
Transaction Date – the date of the cash movement.
Teller Name – the employee processing the transaction.
Transaction Type – whether it was Credit, Debit, Deposit, or Withdrawal.
Amount – the value of the transaction.
This dataset is realistic and representative of daily banking operations, making it ideal for analysis. The dataset is perfectly sized for Tableau analysis, offering clear insights into teller cash handling, workload distribution, and operational trends.
By understanding the structure of the data, I could derive meaningful measures like Cash Inflow and Cash Outflow which became the core of the visualization.
Deriving Key Measures for Tableau Visualization
Before building the visualization, I needed to transform raw transaction data into actionable measures that accurately reflect teller activity. Two primary measures were derived:
Cash Inflow: Includes all transactions classified as Credit, Deposit, or In. This measure represents the total cash coming into the bank through each teller.
Cash Outflow: Includes all transactions classified as Debit, Withdrawal, or Out. This measure represents the total cash leaving the bank through each teller.
Creating these measures at the row level in Tableau ensures accurate aggregation and avoids calculation errors when visualizing by teller. By focusing on cash inflow and outflow, the visualization directly highlights operational performance, workload distribution, and transaction patterns.
Row-level calculations in Tableau for this project
Cash Inflow
IF [Transaction Type] = "Credit"
OR [Transaction Type] = "Deposit"
OR [Transaction Type] = "In"
THEN FLOAT([Amount])
ELSE 0
END
Cash Outflow
IF [Transaction Type] = "Debit"
OR [Transaction Type] = "Withdrawal"
OR [Transaction Type] = "Out"
THEN ABS(FLOAT([Amount]))
ELSE 0
END
Cash Inflow and Cash Outflow: The Core Measures
These two measures form the foundation of the visualization. They answer a simple but essential question:
How much cash did each teller handled during the month?
This is critical in banking operations, helping identify high-volume tellers, monitor workload, and ensure operational efficiency. Even without fancy metrics, visualizing inflow and outflow tells a story.
Creating an Interactive Tableau Dashboard for Cash Flow Analysis
I used a bar chart leveraging Measure Names and Measure Values to visualize Cash Inflow and Cash Outflow per teller. This approach allows for a direct comparison of cash handled by each teller in a single, concise view.
Steps for constructing the visualization in Tableau:
Drag Teller Name to the Columns shelf to represent individual tellers.
Drag Measure Values to the Rows shelf to display aggregated cash amounts.
Drag Measure Names to Color to distinguish between inflow and outflow.
Apply a filter to include only Cash Inflow and Cash Outflow.
Sort tellers by transaction volume to highlight high-activity personnel.
This design results in a clear, intuitive visualization that communicates key operational insights effectively. It is both user-friendly and actionable for bank managers, providing a reliable overview of teller performance and cash handling patterns.
Bar-chart visualization of cash inflow and outflow
Insights from the Visualization
This bar chart provides the meaningful insights of:
Tellers with the highest inflow and outflow which are immediately visible.
Patterns in cash handling become apparent at a glance.
Uneven workloads or unusual transaction volumes stand out
This demonstrates that Tableau can communicate operational data visually without requiring complex dashboards or advanced calculations.
The Power of Focused Visualization
Well-designed, focused charts deliver insights faster and more clearly than overcomplicated visuals.
This bar chart effectively communicates monthly teller cash performance.
Readers can immediately spot patterns, trends, and outliers.
Focusing on key metrics in a clear visualization is often more impactful than adding unnecessary complexity.
Key Takeaways from the Tableau Project
This Tableau project highlighted several important lessons for anyone learning data visualization:
Step-by-step visualization matters: Focusing on one clear objective ensures meaningful insights without confusion.
Row-level calculations are effective: Measures derived at the transaction level avoid aggregation errors.
Clarity drives understanding: A well-structured bar chart communicates performance and patterns better than complex visuals.
Business context is essential: Understanding teller cash activity and operations is more valuable than flashy charts.
Practical problem-solving over memorization: For non-IT professionals, the goal is translating real time raw data into actionable insights.
Action intake from this tableau project
This final blog demonstrates how raw transactional data can be transformed into actionable insights using Tableau:
Focused visualization: Cash inflow and outflow for each teller are displayed in a clear, interactive bar chart.
Practical insights: Even without complex dashboards, patterns, trends, and workload distribution are visible at a glance.
Clarity over complexity: Well-structured visuals communicate operational performance more effectively than large reports.
For anyone starting their analytics journey, you don’t need an IT background to begin. Start with key metrics, focus on clear visualizations, and your analytical skills will grow naturally.
This marks the final chapter of my blogathon series, but the lessons learned is turning data into meaningful insights and creating effective visualizations will last a lifetime.
