Discrimination of Financial Fraud in Transaction Data via Improved Mamba-Based Sequence Modeling

This paper addresses the complexity and high-risk nature of fraud detection in financial transaction data by proposing a discrimination method based on an improved Mamba architecture. The study first performs data cleaning, normalization, and embedding to reduce scale differences across multimodal features, and employs temporal encoding and positional embedding to strengthen sequence representation. A dynamic state updating and selective state space mechanism is then introduced to capture long-term dependencies and global contextual features, thereby enhancing the model's discrimination ability in complex transaction environments. Furthermore, context interaction and weighted aggregation modules are designed to integrate hidden states across time steps into a global representation, providing more robust inputs for final classification. Experiments, including baseline comparisons, sequence length sensitivity, and noise robustness tests, demonstrate that the proposed method outperforms multiple mainstream architectures in Accuracy, F1-score, AUC, and Precision, while showing greater stability in handling data imbalance and adversarial perturbations. Overall, the study confirms the effectiveness and applicability of the improved Mamba architecture for financial fraud detection and provides technical support for building secure and trustworthy risk control systems.