💳 Credit Card Financial Analysis Dashboard

A dynamic and insightful Power BI dashboard built using Excel + SQL, focused on analyzing credit card customer behavior, transaction trends, and revenue performance across various customer segments.

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📌 Project Objective

Build an interactive weekly dashboard that enables stakeholders to:

• Monitor revenue growth and KPIs
• Understand customer demographics and card performance
• Compare week-state-wise contributions
• Track weekly and quarterly changes in revenue and transaction volume

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🧰 Tools & Technologies

Tool	Purpose
Power BI	Dashboard development
Excel	Initial data source formatting
SQL	Data cleaning and management
DAX	Calculated columns & measures

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📁 Dataset Overview

• Customer Data: Age, Gender, Income, Education, Marital Status, Job, Dependents
• Transaction Data: Card Category, Interest, Fees, Total Transaction Amount, Week
• Weekly Revenue Data: Calculated Revenue, WoW changes, Activation & Delinquency rates

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🛠️ Data Preparation Steps

1. Cleaned raw .csv files and imported into SQL tables
2. Connected Power BI to SQL database
3. Created DAX measures for:
   • Revenue = annual_fees + total_trans_amt + interest_earned
   • Week-on-week change in revenue
   • Grouping by age and income
4. Applied filters and slicers (Quarter, Gender, State, Card Type, etc.)

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📊 Key Dashboard Insights

• Total Revenue: ₹57M
• Transaction Volume: ₹46M
• Total Interest Earned: ₹8M
• Top Contributors:
  • Age Group: 30–40 years
  • Income Group: Medium
  • Card Category: Blue (dominates with 83%+ share)
  • Top States: TX, NY, CA (68% combined revenue)
• Gender Revenue Split:
  • Male: ₹31M
  • Female: ₹26M
• Delinquency Rate: 6.06%
• Activation Rate: 57.5%

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📈 Visuals Included

• Revenue by Age Group, Income Group, Education & Marital Status
• Transaction Type (Swipe, Chip, Online)
• Card Category Comparison (Blue, Silver, Gold, Platinum)
• Weekly & Quarterly Revenue Trends
• Revenue by State & Gender

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🧾 Sample DAX Calculations

AgeGroup = SWITCH(
    TRUE(),
    'cust_detail'[customer_age] < 30, "20-30",
    'cust_detail'[customer_age] >= 30 && 'cust_detail'[customer_age] < 40, "30-40",
    'cust_detail'[customer_age] >= 40 && 'cust_detail'[customer_age] < 50, "40-50",
    'cust_detail'[customer_age] >= 50 && 'cust_detail'[customer_age] < 60, "50-60",
    'cust_detail'[customer_age] >= 60, "60+",
    "Unknown"
)

Revenue = 'cc_detail'[annual_fees] + 'cc_detail'[total_trans_amt] + 'cc_detail'[interest_earned]

📁 CreditCardDashboard/
├── Credit_Card_Dashboard.pbix
├── credit_card_customers.csv
├── credit_card_transactions.csv
├── SQL_Table_Scripts.sql
├── DAX_Formulas.txt
└── README.md

👩‍💻 Author
Snehal Nalawade
📧 snehalrnalawade2003@gmail.com
🔗 https://www.linkedin.com/in/snehal-nalawade-834010262 
🌐 https://Snehal027.github.io/Myportfolio/