Credit Card Fraud Detection Based on Machine Learning Algorithms

With the popularization of electronic payments and consumer credit, credit cards have become the core tool for global financial transactions, with transaction volumes exceeding $50 trillion by 2024. But at the same time as transaction growth, fraudulent methods have shown intelligent and covert characteristics, with frequent occurrences of theft, identity fraud, and false transactions. According to industry reports, global credit card fraud losses will exceed $35 billion in 2024, and fraud patterns will continue to evolve with the iteration of payment technology. To overcome the bottleneck of existing algorithms, this paper proposes a Transformer model that integrates LSTM and Kernel Extreme Learning Machine (KELM) optimization. In the study, correlation analysis was first conducted on various indicators of the data, and then multiple comparative models were used for testing to compare their effectiveness. The experimental results showed that the LSTM-KELM Transformer performed the best in all evaluation metrics, with accuracy, recall, precision, and F1 values reaching 0.999, and AUC reaching 1, significantly better than other models. Among other models, Random Forest, Adaboost, CatBoost, and BP neural network have similar performance, with Accuracy, Recall, and Precision mostly around 0.992, slightly lower F1 values, and AUC between 0.998-0.999, belonging to the suboptimal level; The various indicators of logistic regression are slightly weaker than the above model, about 0.99; The performance of Decision Tree and Gradient Boosting Tree (GBDT) is relatively poor, especially with GBDT's Accuracy, Recall and other indicators only reaching 0.985, and Decision Tree's AUC also only reaching 0.985. Overall, the LSTM-KELM Transformer outperforms other models in terms of classification accuracy, recognition ability for positive and negative samples, comprehensive performance, and ability to distinguish positive and negative classes, demonstrating stronger classification advantages and contributing to the correct classification of fraud detection. This has important practical significance for improving the efficiency of credit card fraud detection and reducing financial losses.