Interactive Power BI analytics report exploring 2024 UPI transaction trends, balance dynamics, demographic segmentation, and regional distribution using Excel data and DAX modeling.
The default view highlights Monthly Transaction Amounts (2024) using a line chart to visualize seasonality, volatility, and trend movement.
Users can dynamically toggle between:
- Line Chart – Transaction Amount
- Column Chart – Transaction Amount
- Line Chart – Remaining Balance
- Column Chart – Remaining Balance
Implemented using Bookmarks + Selection Pane to maintain a single-page dynamic visualization architecture.
This page provides:
- Monthly Amount & Remaining Balance comparison
- City segmentation (Bangalore, Delhi, Hyderabad, Mumbai)
- Currency-level analysis (INR, USD, EUR, GBP)
Digital payment systems generate high-frequency transactional data that requires structured analysis to identify behavioral patterns and financial trends.
This report analyzes UPI transaction data to:
- Identify monthly transaction trends
- Monitor balance stability patterns
- Segment users demographically
- Compare financial distribution across cities and currencies
The goal is to transform transactional payment data into structured financial insights through interactive reporting.
- Loaded Excel dataset into Power BI Desktop
- Enabled Column Distribution, Column Quality & Column Profile
- Applied full dataset profiling
- Validated and corrected data types
- Structured fields for analytical aggregation
Age Groups =
IF('UPI Transactions'[CustomerAge] <= 25, "A1",
IF('UPI Transactions'[CustomerAge] <= 35, "A2",
"A3"))
Segments customers into three demographic categories:
- A1 (≤25 years)
- A2 (26–35 years)
- A3 (>35 years)
Total Amount =
SUM('UPI Transactions'[Amount])
Remaining Balance =
SUM('UPI Transactions'[Remaining Balance])
These measures power monthly trend visuals and matrix aggregation.
- Transactions by Month (Line & Column)
- Remaining Balance by Month (Line & Column)
- Bookmark-driven visualization switching
- Multi-dimensional slicers:
- BankNameSent
- BankNameReceived
- City
- DeviceType
- Gender
- Age Groups
- MerchantName
- PaymentMethod
- Purpose
- TransactionType
Bookmark implementation ensures dynamic switching without page duplication.
- Matrix view displaying:
- Amount
- Remaining Balance
- Month segmentation
- City & Currency comparison
- Granular filtering support
- Transaction volumes show identifiable seasonal peaks, suggesting event-driven or time-bound spending behavior.
- Remaining balance demonstrates moderate month-over-month variation, indicating consistent inflow-outflow patterns.
- Age segmentation enables demographic-based transaction comparison.
- Regional and currency segmentation enables comparative financial monitoring across operational locations.
- Bookmark-driven switching enhances report interactivity and usability.
- Excel data integration in Power BI
- Data profiling & validation
- DAX calculated columns & measures
- Financial KPI aggregation
- Bookmark Navigator implementation
- Matrix-based aggregation
- Multi-dimensional filtering
- Interactive Power BI report architecture design
This project demonstrates strong fundamentals in:
- Financial trend analysis
- Demographic segmentation
- Interactive report architecture
- DAX-based data modeling
The report simulates a real-world digital payment monitoring solution focused on trend identification, segmentation logic, and structured financial reporting.