Credit Card Fraud Detection Algorithm Based on a Stacked Ensemble Model

The widespread global adoption of credit card payments has significantly increased convenience for consumers. However, this shift has also heightened the demand for robust fraud detection systems across various financial institutions. While numerous fraud detection algorithms have been proposed in both academia and industry, significant challenges remain in the accurate identification of minority class samples and the effective capture of time series information during feature extraction. To address these challenges, this paper proposes a long short-term memory-logistic regression stacked ensemble model (LLSE) that integrates long short-term memory (LSTM) and logistic regression (LR). The first layer of the model extracts time series features by combining three independently trained LSTM base learners whose outputs are concatenated and passed to the second layer, an LR meta-learner, to generate the final predictions. This dual-layer decision-making mechanism effectively captures spatiotemporal correlation features in fraudulent transactions, thereby enhancing the overall performance of the model. The model’s performance is evaluated via two publicly available credit card datasets. This study innovatively combines the TS-N2VA feature extraction model with the LLSE classifier to comprehensively enhance credit card fraud detection performance through both hidden feature extraction and classifier improvement. Experiments on two public credit card datasets demonstrate that the LLSE classifier achieves significantly better performance in identifying minority class samples while maintaining overall recognition accuracy, outperforming other fraud detection models in the experiments.