Comparative Fraud Detection Using Machine Learning Models

This project focused on developing a robust fraud detection system using machine learning techniques. Financial fraud is a significant concern for businesses and consumers alike, with billions lost annually to fraudulent activities. Early detection is crucial to minimize losses and protect stakeholders.

The project involved analyzing historical transaction data, identifying patterns associated with fraudulent activities, and building machine learning models capable of accurately flagging suspicious transactions in real-time.

• Dealing with highly imbalanced datasets where fraudulent transactions represent less than 0.5% of all transactions
• Ensuring high recall (catching most frauds) without sacrificing precision (avoiding false positives)
• Processing and analyzing large volumes of transaction data efficiently
• Developing models that can adapt to evolving fraud patterns and techniques
• Creating a system that can make predictions in near real-time to prevent fraud before it completes

I implemented a comprehensive approach to address the fraud detection challenge:

1. Data Preprocessing: Applied advanced techniques to handle the class imbalance, including SMOTE (Synthetic Minority Over-sampling Technique) and class weighting.
2. Feature Engineering: Created relevant features from transaction data, including temporal patterns, frequency analysis, and behavioral indicators.
3. Model Comparison: Implemented and compared multiple classification algorithms including:
   • Random Forest
   • Gradient Boosting (XGBoost)
   • Deep Learning (Neural Networks)
   • Logistic Regression (as baseline)
4. Hyperparameter Tuning: Used grid search and cross-validation to optimize model parameters for the best performance.
5. Evaluation Framework: Developed a custom evaluation framework focusing on precision-recall balance and business impact metrics.