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UPI Transactions Analytics – Power BI

Interactive Power BI analytics report exploring 2024 UPI transaction trends, balance dynamics, demographic segmentation, and regional distribution using Excel data and DAX modeling.

📊 Report Preview

Monthly Transaction Trend (Bookmark Enabled)

Image

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.

City & Currency Breakdown (Matrix View)

Image

This page provides:

  • Monthly Amount & Remaining Balance comparison
  • City segmentation (Bangalore, Delhi, Hyderabad, Mumbai)
  • Currency-level analysis (INR, USD, EUR, GBP)

Problem Statement

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.

Data Preparation

  • 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

DAX Implementation

Calculated Column – Age Segmentation

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)

Core Measures

Total Amount =
SUM('UPI Transactions'[Amount])

Remaining Balance =
SUM('UPI Transactions'[Remaining Balance])

These measures power monthly trend visuals and matrix aggregation.

Report Architecture

Page 1 – Monthly Trend Analysis

  • 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.

Page 2 – City & Currency Financial Breakdown

  • Matrix view displaying:
    • Amount
    • Remaining Balance
  • Month segmentation
  • City & Currency comparison
  • Granular filtering support

Key Insights

  • 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.

Technical Skills Demonstrated

  • 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

Conclusion

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.