KYC AML Compliance Data Analysis

This project simulates the KYC/AML risk assessment workflow used by compliance teams in regulated financial institutions. It covers customer risk scoring, suspicious transaction detection, and management reporting the same analytical pipeline used in AML monitoring functions at banks and consultancies..

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.