https://github.com/faisal-khann/banking_portfolio_risk_analysis
This analysis explores the banking customer dataset to uncover key patterns in account holdings, loan portfolios, income profiles, and transactional behavior. By integrating both financial and demographic variables, we examine customer engagement, lending trends, and risk exposure across different segments.
https://github.com/faisal-khann/banking_portfolio_risk_analysis
data-manipulation eda matplotlib numpy pandas powerbi python seaborn
Last synced: 12 months ago
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This analysis explores the banking customer dataset to uncover key patterns in account holdings, loan portfolios, income profiles, and transactional behavior. By integrating both financial and demographic variables, we examine customer engagement, lending trends, and risk exposure across different segments.
- Host: GitHub
- URL: https://github.com/faisal-khann/banking_portfolio_risk_analysis
- Owner: Faisal-khann
- Created: 2025-08-09T16:15:58.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-08-13T09:52:36.000Z (12 months ago)
- Last Synced: 2025-08-13T10:27:18.965Z (12 months ago)
- Topics: data-manipulation, eda, matplotlib, numpy, pandas, powerbi, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 12.6 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Banking Customer Behavior and Financial Performance Analysis
Performed end-to-end data analysis and built an interactive dashboard based **SQL**, **Python**, and **Power BI** to deliver insights into banking customer behavior, portfolio performance, and risk indicators using real-world financial metrics
---
## 📌 Table of Contents
- [Overview](#Overview)
- [Business Problem](#business-problem)
- [Project Workflow](#Project-Workflow)
- [Dataset](#dataset)
- [Tools & Technologies](#tools--technologies)
- [Detail Analysis Preview](#Detail-Analysis-Preview)
- [Dashboard Preview](#Dashboard-Preview)
- [Project Structure](#Project-Structure)
- [How to Run This Project](#How-to-Run-This-Project)
- [Author & Contact](#author--contact)
---
## Overview
This analysis explores the banking customer dataset to uncover key patterns in account holdings, loan portfolios, income profiles, and transactional behavior. By integrating both financial and demographic variables, we examine customer engagement, lending trends, and risk exposure across different segments. The insights aim to support data-driven decision-making for improving product strategies, managing portfolio risk, and enhancing overall banking performance
---
## Business Problem
This project addresses a **Bank Performance & Customer Segmentation** challenge.
The objective is to monitor, analyze, and visualize key banking metrics to drive informed decision-making.
### Key Questions
- **Customer Base Analysis** – How many clients does the bank have? Which customer segments (e.g., income band, nationality) contribute the most value?
- **Engagement & Retention** – How engaged are customers based on total engagement accounts and credit card accounts?
- **Financial Performance Tracking** – How well is the bank performing in terms of loans, deposits, and fee generation?
- **Goal Achievement** – Is the bank meeting its financial targets?
- **Portfolio Risk & Opportunity** – Are loans concentrated in specific income bands or nationalities, indicating potential risks or opportunities?
- **Age Distribution** - How can the bank optimize its product offerings, marketing strategies, and customer retention plans across different age groups—particularly addressing the underrepresentation of customers under 25 and over 60—while reducing overreliance on the 30–45 age segment?
The goal is to perform end-to-end data analysis to uncover in-depth insights and, subsequently, create a **real-time banking performance dashboard** that identifies trends, highlights risks, and uncovers opportunities for growth.
---
## Project Workflow
---
## Tools & Technologies
| Tool | Purpose |
|-------------|----------------------------------|
| **Python** | Data analysis & scripting |
| **Pandas** | Data manipulation |
| **SQL** | Data extraction from MySql |
| **Power BI**| Dashboard creation |
| **Jupyter** | EDA & visualization |
| **Matplotlib/Seaborn** | Visual analytics |
---
## Dataset
- Multiple CSV files located in /dataset/ folder
- 'Banking.csv' located in the /notebook/ folder
---
## Project Structure
```
Banking Customer Behavior and Financial Performance Analysis/
│
├── README.md
├── .gitignore
├── requirements.txt
├── Banking dashboard Report.pdf
│
├── notebooks/ # Jupyter notebooks
│ ├── Banking-risk-analysis.ipynb
│
├── dashboard/ # Power BI dashboard file
│ └── Banking_Analysis.pbix
├── Report-Ppt/ # Power BI dashboard ppt & report
│ └── Banking dashboard ppt
└── Banking dashboard report
```
---
## Detail Analysis Preview
📁 Notebook: [`Banking-risk-analysis.ipynb`](https://github.com/Faisal-khann/Banking_Portfolio_Risk_Analysis/blob/main/notebook/Banking-risk-analysis.ipynb)
I performed end-to-end financial analysis project where I collected, cleaned, and analyzed financial data to extract meaningful insights. The project involved performing exploratory data analysis, calculating key financial metrics, and identifying trends to support strategic decision-making
## Dashboard Preview
📁 Live Dashboard: [`Banking_Analysis.pbix`](https://app.powerbi.com/view?r=eyJrIjoiMTk3MjE2MjItMTdhZi00NjkwLTg1MzAtZTUxZDNiYjJkMDlkIiwidCI6IjQyYjUxMzUzLTZhMzctNDA5Zi1hMmZlLTc3OGE5YmUzMTllNCJ9)
After completing the end-to-end financial analysis, I built an interactive Power BI dashboard that provides a comprehensive view of the banking sector. The dashboard highlights key insights across loans, deposits, credit cards, and customer engagement. By segmenting data by nationality, income bands, and engagement timeframe, it enables stakeholders to monitor lending performance, deposit patterns, and customer demographics in real-time, ultimately supporting data-driven strategies and improving business outcomes.
---
## How to Run This Project
1. Clone the repository:
git clone https://github.com/yourusername/Banking_Portfolio_Risk_Analysis.git
2. Open and Run Notebooks
notebooks/Banking-risk-analysis.ipynb`
3. Open Power BI Dashboard:
dashboard/Banking_Analysis.pbix
---
## Author & Contact
**Faisal Khan**
*Data Analyst*
For any questions, collaboration opportunities, or project-related inquiries, feel free to reach out:
- 📧 [Email](mailto:thisside.faisalkhan@example.com)
- 💼 [LinkedIn](http://www.linkedin.com/in/faisal-khan-332b882bb)
Let’s connect and build something impactful!