Fraud Detection Analysis

📌 Objective

Analyze transaction data to identify fraud patterns and risk indicators using SQL and Python.

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📊 Context

This project simulates real-world fraud analysis scenarios, focusing on transaction monitoring, anomaly detection and risk evaluation.

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🛠️ Technologies

• SQL (PostgreSQL)
• Python (Pandas)
• Jupyter Notebook

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🔍 Analysis Performed

• Identification of suspicious transactions based on value and frequency
• Creation of fraud detection rules
• Calculation of anomaly rate
• Exploratory Data Analysis (EDA)

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📈 Key Insights

• High-value transactions concentrated in short time intervals indicate risk patterns
• Repeated transactions from same user increase fraud probability
• Behavioral patterns are critical for fraud detection

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🚀 How to Run

1. Clone repository
2. Open notebooks
3. Run analysis

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🎯 Learnings

• Applied SQL and Python in real fraud scenarios
• Developed analytical thinking for risk identification
• Built structured data analysis pipeline