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🏦 Banking Customer Intelligence Dashboard

An end-to-end data analytics project that explores banking customer behavior, loans, deposits, and financial performance using real-world structured data.
Built with Power BI, Python (Pandas/NumPy/Matplotlib), and SQL, this project transforms raw banking data into actionable business insights.

📸 Screenshots

🏠 Home Dashboard

  • Overview of total clients, loans, deposits, and accounts
  • Screenshot 2026-03-22 171517

💳 Loan Analysis

  • Loan distribution by income band, occupation, and banking relationship
  • Screenshot 2026-03-22 171544

💰 Deposit Analysis

  • Deposit trends by income group, nationality, and relationship type
  • Screenshot 2026-03-22 171606

📊 Summary Dashboard

  • High-level KPIs and performance metrics
  • Screenshot 2026-03-22 171624

❓ Q&A Dashboard

  • Uses Power BI Q&A feature for instant insights
  • Screenshot 2026-03-22 171650

✨ Project Highlights

Metric Value Description
Total Clients 1.5K+ Total number of banking customers
Total Loan 4.38bn Total loan amount issued
Total Deposit 3.77bn Total deposits across accounts
Business Lending 2.60bn Loans given to businesses
Checking Accounts 963.28M Total checking account balance
Saving Accounts 698.73M Total savings balance
Foreign Currency 45.02M International account holdings

🔍 Key Insights

  • 📈 High Loan Concentration in specific Banking Relationships (Commercial & Institutional)
  • 💰 Deposits are highest in medium-income customers
  • 👩 Gender-based analysis shows variation in loan and deposit behavior
  • 🌍 Nationality trends highlight key revenue-generating segments
  • 💼 Business lending dominates overall loan portfolio
  • 📊 Customer engagement strongly linked with deposits + loans combined

🔄 Workflow

1. Data Collection

  • Imported structured banking dataset (multiple tables)

2. Data Cleaning & Transformation (SQL + Python)

  • Removed duplicates and handled missing values
  • Standardized column names and formats
  • Created derived columns:
    • Total Loan
    • Total Deposit
    • Engagement Score

3. Data Analysis (Python)

  • Used Pandas & NumPy for data manipulation
  • Performed:
    • Distribution analysis
    • Trend identification
    • Outlier detection

4. Data Modeling (SQL)

  • Joined multiple tables
  • Created relationships and aggregations
  • Optimized queries for reporting

5. Visualization (Power BI)

  • Designed interactive dashboards with:
    • KPI Cards
    • Bar Charts
    • Donut Charts
    • Filters (Gender, Year, Banking Relationship)

6. Insight Generation

  • Identified high-value customers
  • Analyzed loan vs deposit patterns
  • Segmented customers based on behavior

🛠️ Tech Stack

Layer Tools / Technologies
Data Processing Python (Pandas, NumPy)
Data Analysis Python, SQL
Visualization Power BI
Database SQL
Storage Excel / CSV
Version Control Git, GitHub

🚀 Getting Started

Prerequisites:

  • Power BI Desktop
  • Python 3.8+
  • SQL (MySQL / PostgreSQL / SQL Server)

Steps:

git clone https://github.com/Vedant-Kharwade

📌 Features

  • Interactive filtering (Year, Gender, Banking Type)
  • Clean UI with professional layout
  • Real-time KPI tracking
  • Multi-dashboard navigation
  • Business-focused insights

Project Link:

https://github.com/Vedant-Kharwade/Banking-Analysis-Project-MS-SQL-Python-Power-Bi-

📬 Contact

📧 vedantkharwade123@gmail.com

🔗 https://www.linkedin.com/in/vedant-kharwade-45b82224b/

⭐ If you found this project useful, consider giving it a star!