An Intelligent Real-Time Credit Card Fraud Detection Framework Using Machine Learning
Keywords:
Credit Card Fraud Detection, Machine Learning, Light Gradient Boosting Machine (LGBM), ADASYN Oversampling, Feature Engineering, FastAPI Web ServiceAbstract
In this report, we present the development and implementation of a Real-Time Credit Card Fraud Detection System using machine learning techniques. The exponential growth of electronic transactions demands robust and instantaneous fraud detection mechanisms. The main aim of the project was to construct a highly scalable and accurate system for real-time classification of transactions as legit or fraudulent. The methodology We had a very imbalanced transaction dataset. We did feature engineering to extract contextual variables like transaction history, a custom risk score. To tackle the class imbalance problem, ADASYN oversampling technique was applied. We chose the Light Gradient Boosting Machine (LGBM) model as it performed best for binary classification problems in both speed and accuracy. The model was then trained and deployed using a FastAPI web service with a PostgreSQL database for prediction logging and Redis caching to enhance performance and latency in production. The validation results showed high Recall and F1-scores, indicating that the system is effective in detecting fraudulent activities with low false negatives. -scores which proved that the system is capable of detecting fraudulent activities with low false negatives.
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