Credit Card Financial Dashboard

Customer Report

Transaction Report

Table of Contents

1. Project Objective
2. Data from SQL
3. Data Processing & DAX
4. Dashboard & Insights
5. Export & Share Project
6. Project Insights
7. Add to Resume

Project Objective

The objective of this project is to develop a comprehensive Credit Card Weekly Dashboard that provides real-time insights into key performance metrics and trends, enabling stakeholders to effectively monitor and analyze credit card operations. The dashboard will help decision-makers identify revenue opportunities, track customer behavior, and manage overall performance.

Data from SQL

The project utilizes data stored in a PostgreSQL database. Key steps involved:

1. Prepare CSV Files: Import data related to credit card transactions and customer details.
2. Create Tables in SQL: Use SQL queries to create relevant tables such as cc_detail and cust_detail.
3. Import CSV Files into SQL: Load the prepared CSV files into the created SQL tables.

Data Processing & DAX

In Power BI, DAX (Data Analysis Expressions) queries were used to process and calculate important metrics such as customer age groups, income categories, weekly revenue, and comparisons. Some key DAX queries include:

-- Age Group Classification
AgeGroup = SWITCH(
    TRUE(),
    'public cust_detail'[customer_age] < 30, "20-30",
    'public cust_detail'[customer_age] >= 30 && 'public cust_detail'[customer_age] < 40, "30-40",
    'public cust_detail'[customer_age] >= 40 && 'public cust_detail'[customer_age] < 50, "40-50",
    'public cust_detail'[customer_age] >= 50 && 'public cust_detail'[customer_age] < 60, "50-60",
    'public cust_detail'[customer_age] >= 60, "60+",
    "unknown"
)

-- Income Group Classification
IncomeGroup = SWITCH(
    TRUE(),
    'public cust_detail'[income] < 35000, "Low",
    'public cust_detail'[income] >= 35000 && 'public cust_detail'[income] <70000, "Med",
    'public cust_detail'[income] >= 70000, "High",
    "unknown"
)

-- Weekly Revenue Calculation
week_num2 = WEEKNUM('public cc_detail'[week_start_date])
Revenue = 'public cc_detail'[annual_fees] + 'public cc_detail'[total_trans_amt] + 'public cc_detail'[interest_earned]

Current_week_Reveneue = CALCULATE(
    SUM('public cc_detail'[Revenue]),
    FILTER(
        ALL('public cc_detail'),
        'public cc_detail'[week_num2] = MAX('public cc_detail'[week_num2])
    )
)

Previous_week_Reveneue = CALCULATE(
    SUM('public cc_detail'[Revenue]),
    FILTER(
        ALL('public cc_detail'),
        'public cc_detail'[week_num2] = MAX('public cc_detail'[week_num2]) - 1
    )
)

Dashboard & Insights

Weekly Insights (Week 53 - 31st Dec):

• Revenue: Increased by 28.8%
• Total Transaction Amount & Count: Increased by xx% & xx%
• Customer Count: Increased by xx%

Year-To-Date Overview:

• Total Revenue: $57M
• Total Interest: $8M
• Total Transaction Amount: $46M

Gender Insights:

• Male Customers: $31M
• Female Customers: $26M

Card Performance:

• Blue & Silver cards: Contribute to 93% of overall transactions.

Top States:

• Texas, New York, and California: Contribute 68% of total transactions.

Additional Metrics:

• Overall Activation Rate: 57.5 %
• Overall Delinquent Rate: 6.06 %

Export & Share Project

To share the project with stakeholders:

1. Export the dashboard from Power BI as a .pbix file.
2. Share the .pbix file or publish it on the Power BI service for access.
3. Schedule regular updates for the dashboard to ensure real-time insights.

Project Insights

This project provides stakeholders with a comprehensive view of credit card operations, allowing for the effective monitoring of key metrics such as revenue growth, customer segmentation, and transaction performance.

Add to Resume

Credit Card Financial Dashboard using Power BI:

• Developed an interactive dashboard using transaction and customer data from a SQL database to provide real-time insights.
• Streamlined data processing & analysis to monitor key performance metrics and trends.
• Shared actionable insights with stakeholders based on dashboard findings to support decision-making processes.