This study addresses the challenges of highly concealed abnormal behaviors, scarce fraud samples, and complex high-dimensional feature structures in credit card transactions by proposing a fraud detection framework that integrates a generative prior. The method first uses a generative model to learn the latent distribution of normal transactions and builds a stable behavioral baseline, where distributional constraints strengthen the structural representation of normal patterns. The latent variables from the generative model are then embedded jointly with discriminative features, allowing the model to capture both local attributes and global behavioral structures within a unified representation space and thereby improving the detection of weak anomalies and structural deviations. To evaluate performance comprehensively, the study conducts multidimensional experiments, including hyperparameter sensitivity, environmental disturbance sensitivity, and data disturbance sensitivity, to analyze how the generative prior affects model stability and discriminative capability under different external conditions. The results show that the framework outperforms traditional methods across Accuracy, Precision, Recall, and AUC, and maintains higher robustness and consistency in noisy, imbalanced, and dynamic transaction environments. The findings demonstrate that integrating generative distribution modeling with discriminative risk representation significantly enhances the model's sensitivity to abnormal behaviors and provides a more structured and reliable solution for risk identification in complex financial settings.

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.

This study addresses the challenges of financial risk early warning by proposing a modeling approach based on spatiotemporal Transformers. The research first examines the multidimensional characteristics of financial risk, emphasizing its temporal dynamics and cross-regional interactions. It notes that many existing methods struggle to jointly capture temporal dependencies and inter-regional risk transmission patterns. To overcome these limitations, a unified spatiotemporal modeling framework is developed. The framework integrates temporal encoding, spatial adjacency information, and multi-head attention mechanisms to model long-range dependencies and regional spillover effects. In the model architecture, an embedding layer is employed to learn representations from multi-source financial indicators. A self-attention mechanism facilitates global feature interaction, while a graph convolution component further enhances the modeling of spatial relationships across markets. The final risk representation is generated through a feed-forward network with normalization layers, providing a structured basis for financial risk assessment and early warning analysis. Experimental evaluations include comparative studies and sensitivity analyses under varying missing data ratios, time window settings, and environmental conditions. The results indicate that the proposed method consistently outperforms several baseline models in terms of accuracy, precision, recall, and F1-score. Overall, the approach demonstrates strong robustness and practical applicability in complex financial settings, offering an effective tool for financial risk monitoring and decision support.