Using Machine Learning To Detect Credit Card Fraud

Credit card fraud, a pressing concern in the digital age, necessitates advanced detection mechanisms. This research delves into machine learning solutions, particularly the Random Forest classifier, to discern fraudulent transactions. After addressing inherent class imbalances in transaction datasets, our model achieved remarkable accuracy, outperforming baseline models. Notably, our approach introduces novel methodologies in preprocessing and model interpretation, setting new industry benchmarks. The findings emphasize specific transactional features as key fraud indicators, offering financial institutions a refined lens for fraud detection.