Credit Card Fraud Detection

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My Role

Machine Learning Engineer – Security & Risk Analytics

  • Synthetic Data Engineering: Simulating realistic financial datasets with 5% fraud distribution
  • Feature Risk Scoring: Developing "Location Risk" and "Device Risk" variables
  • Statistical Distribution Analysis: Histograms for normal vs fraudulent transaction patterns
  • Model Training & Optimization: Implementing Logistic Regression for fraud classification
  • Performance Validation: Engineering visual Confusion Matrix for false positive/negative analysis

Project Highlights

  • Cybersecurity Focus: Demonstrates AI application for sensitive data protection
  • Feature Engineering: Proves context (device & location) improves predictive power
  • High-Precision Evaluation: Focus on accuracy while minimizing false positives
  • Clean Code Standards: Uses seeded random generation for scientific reproducibility
  • Real-World Simulation: Addresses imbalanced data challenges in fraud detection
GitHub Repository

Credit Card Fraud Detection is a machine learning-based security system designed to identify and flag fraudulent financial transactions in real-time. By analyzing patterns in transaction amounts, geographical location risk, and device-specific risk scores, the model can differentiate between legitimate user behavior and malicious activity.

I developed this project to demonstrate the practical application of Logistic Regression in high-stakes environments where identifying "outlier" behavior is essential for protecting financial assets and preventing unauthorized transactions.

The project implements a comprehensive cybersecurity analytics pipeline:

  1. Data Simulation: Creating realistic synthetic financial transaction datasets
  2. Risk Feature Engineering: Developing location and device risk scoring algorithms
  3. Statistical Analysis: Distribution analysis of transaction patterns
  4. Imbalanced Learning: Handling 5% fraud distribution in training data
  5. Model Optimization: Logistic Regression for binary fraud classification
  6. Performance Validation: Confusion matrix analysis for false positive management

Technologies Used

  • Python 3 – Core language for security logic implementation
  • Scikit-Learn – Predictive engine and performance metrics
  • Pandas & NumPy – Data structuring and statistical generation
  • Matplotlib – Visual diagnostic tools and risk scatter plots
  • Machine Learning Pipelines – Train-Test Split methodology
  • Statistical Analysis – Fraud pattern detection techniques
  • Cybersecurity Algorithms – Anomaly detection methods
  • Synthetic Data Generation – Realistic fraud simulation

Key Features

  • Imbalanced Data Simulation: Addresses real-world fraud distribution
  • Multi-Factor Risk Analysis: Correlates location and device risk factors
  • Visual Diagnostic Suite: Scatter plots for fraud event clustering
  • Automated Decision Logic: Binary classification in milliseconds
  • Confusion Matrix Visualization: Transparent model performance analysis
  • Real-Time Detection: Capable of flagging fraud during transactions
  • Risk Scoring System: Quantifies location and device threat levels
  • Production-Ready Security: Deployable for financial institution use

Security Impact

  • Financial Protection: Prevents unauthorized transactions and financial loss
  • Customer Trust: Enhances security while minimizing legitimate transaction friction
  • Real-Time Prevention: Flags suspicious activity during transaction processing
  • Risk Mitigation: Reduces fraud-related losses for financial institutions
  • Regulatory Compliance: Supports anti-fraud measures required in financial services
View GitHub Repository