Banking Transaction Analysis 🏦

Overview

Analysis of banking transactions using PySpark to detect suspicious high-value activity.

Tools & Technologies

• Python 3.x
• PySpark (Apache Spark)
• Pandas
• Jupyter Notebook

Dataset

• 1000 synthetic banking transactions
• 50 unique accounts
• Date Range: January 2024 – December 2024

Tasks Performed

1. Load Transaction Dataset
2. Detect High-Value Transactions (Threshold: 40,000)
3. Group by Account
4. Calculate Total Balance

Results

• Suspicious Transactions: 194
• Completed Suspicious Transactions: 67
• Suspicious Accounts: 27
• Highest Balance Account: ACC0017 (878,654.32)

Output Files

• suspicious_transactions.csv
• account_summary.csv

How to Run

1. Install dependencies: pip install -r requirements.txt

2. Launch Jupyter Notebook: jupyter notebook

3. Open banking_transaction_analysis.ipynb

4. Run all cells