Credit Card Fraud Detection Based on Machine Learning Prediction
Yang Ge · Advances in computer science research · 2024
In recent years, credit card fraud has become increasingly rampant, posing a major threat to financial security.To effectively detect and prevent credit card fraud, this study combines three machine learning algorithms, namely Random Forest (RS), Support Vector Machine (SVM), and Logistic Regression (LR), to deeply analyze credit card transaction data through cross-validation with different multiplicity.The study results show that Random Forest performs best in terms of precision and F1 scores, SVM performs well in terms of recall, and logistic regression has a high Area Under Curve (AUC) value in distinguishing between fraudulent and non-fraudulent transactions.Through meticulous data preprocessing, feature engineering, and model optimization, this study significantly improves the performance and stability of each model.The research results of this paper provide an important reference for building an efficient and reliable credit card fraud detection system, which has important practical application value and theoretical significance across different sectors and industries.