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Customer, Loan & Deposit Analytics Dashboard

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📌 Project Overview

This project simulates a real-world data analytics solution for a banking institution.

The objective of the project is to analyze customer financial data, loan distribution, and deposit trends to generate meaningful insights that support strategic banking decisions.

The complete workflow includes data analysis using Python, relational data modeling using SQL, and interactive business intelligence dashboards built in Power BI.

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📈 Dashboard Preview

System Architecture

Home Dashboard

Loan Analysis Dashboard

Deposit Analysis Dashboard

Summary Dashboard

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🎯 Business Problem

Banks manage large volumes of financial data related to customers, deposits, loans, and investment relationships.

However, without proper analytics, it becomes difficult to understand:

• Customer financial behavior
• Loan distribution patterns
• Deposit contribution across segments
• Relationship between banking products and customer profiles

The challenge was to transform raw banking datasets into interactive dashboards and actionable business insights.

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📊 Key Business Insights

• Customer Deposit Distribution

  • Analysis revealed variations in deposit contributions across different customer segments.

• Loan Portfolio Analysis

  • Loan distribution insights highlight which categories contribute most to the bank’s lending portfolio.

• Customer Demographics Impact

  • Gender and relationship data provide visibility into customer demographics and their banking behavior.

• Investment Advisory Relationships

  • The dataset reveals how investment advisors are associated with customer investment activities.

• Financial Product Engagement

  • By analyzing deposits, loans, and investments together, the dashboard provides a comprehensive overview of customer engagement with banking products.

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📊 Dataset

The dataset represents simulated banking customer data and financial product relationships.

Dataset Files:

• Banking.csv
• banking-realtionships.csv
• gender.csv
• investment-advisiors.csv

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⚙️ Tools & Technologies Used

• Python (Data Analysis & Exploration)
• Jupyter Notebook
• MySQL (Relational Data Storage & Queries)
• Microsoft Power BI Desktop
• Power Query (Data Transformation)
• Data Modeling (Relationships & Schema Design)
• DAX (Data Analysis Expressions)

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🧱 Workflow Architecture

Data Sources (CSV Files)
→ Data Cleaning & Exploration (Python / Jupyter Notebook)
→ Data Storage & Querying (MySQL)
→ Data Transformation (Power Query in Power BI)
→ Data Modeling & Relationships
→ DAX KPI Calculations
→ Interactive Dashboard Development
→ Business Insights & Reporting

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▶ How to View the Dashboard

1. Download the .pbix file from the /dashboard folder.
2. Open it using Microsoft Power BI Desktop.
3. Explore filters, slicers, and KPI visuals interactively.

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💡 Key Outcomes

• Built a complete end-to-end data analytics workflow
• Performed exploratory data analysis using Python
• Designed relational data relationships for financial datasets
• Developed interactive Power BI dashboards for banking analytics
• Generated insights related to customer deposits, loans, and investment relationships

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🔗 Important Links

• 📖 Medium Blog (Detailed Project Explanation)
  Read the full case study here

• 📊 Project Presentation Dashboard (PPT Slides)
  View presentation slides

• 🎥 YouTube Walkthrough (Dashboard Demo & Explanation)
  Watch the full project demo

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