An examination of machine learning-based credit card fraud detection systems

As a result of the e-commerce industry's explosive growth, credit cards are now frequently used for online purchases. In recent years, banks have faced a significant issue with credit card fraud (CCF) due to the difficulty in detecting fraudulent activity within the credit card system. Machine learning is the solution to the problem of CCFD during transactions. An analysis starts with a study of the Kaggle-provided CCFD dataset. There is a considerable disparity between the classifications in the dataset, which has 284,807 transactions total, of which only 492 are deemed fraudulent. The preprocessing methods are used to prepare the data, which includes the handling of the missing values, the detection of outliers, and the encoding of the categorical variables. Five different classification models are tested and evaluated employing different metrics like precision, F1-score, accuracy, and recall. These models are SVM, Random Forest (RF), Bagging, XGBoost, and DT. In terms of spotting fraudulent transactions, XGBoost is the model that has the highest accuracy rate of 99% among the others. To further strengthen the effectiveness and reliability of fraud detection systems, future research might investigate ensemble methodologies and integrate real-time data streams, guaranteeing thorough defence against financial crime.