KYC AML Compliance Data Analysis

This project demonstrates a mock KYC (Know Your Customer) and AML (Anti-Money Laundering) data analysis workflow using Python and Jupyter/Colab. It showcases how financial institutions can perform customer risk scoring, detect suspicious activity, and generate compliance reporting.

Project Contents

• notebooks/ — Jupyter Notebook with step-by-step analysis
• data/ — Mock KYC/AML datasets (CSV/Excel)
• scripts/ — Python scripts for reusable functions and data processing
• visualizations/ — Charts and graphs generated from the analysis

Key Features

• Load and clean KYC/AML data
• Perform risk scoring on customers
• Identify PEP (Politically Exposed Persons) flagged accounts
• Generate visual dashboards for compliance insights

Tools & Technologies

• Python (Pandas, Matplotlib, NumPy)
• Jupyter Notebook / Google Colab
• CSV / Excel for mock datasets

Getting Started

1. Clone the repository:
2. Open the notebook in Google Colab or Jupyter.
3. Run the cells in order to reproduce the analysis.
4. All files are mock data; no real sensitive information is included.

Disclaimer

This project is for educational and portfolio purposes only. All data is simulated and does not contain any real customer information.