Xinyi Liang

To address the difficulty of maintaining stable structural modeling in intelligent decision systems under complex dynamic environments and the sensitivity of decision behavior to state uncertainty, this study proposes an intelligent decision modeling method based on structure aware learning. The method constructs structured state representations that map environmental observations, state evolution relationships, and decision processes into a unified representation space. This design enables explicit modeling of internal structural constraints and dynamic dependencies within the system. During decision modeling, structural constraints are introduced to guide policy learning, allowing decision behavior to remain consistent and stable under long term optimization objectives. The proposed framework effectively captures state evolution patterns in complex state spaces and mitigates decision instability caused by excessive reliance on local information in traditional methods. Comparative evaluations demonstrate clear advantages in decision utility, decision stability, state transition modeling accuracy, and representation consistency. These results indicate that structure aware learning provides a more reliable modeling foundation for intelligent decision systems. The findings further show that systematically integrating structural information into decision modeling improves overall performance and trustworthiness in complex dynamic scenarios.

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.

This work targets open-world credit risk identification where few labeled samples and emerging fraud scenarios coexist. It proposes a unified detection framework that integrates meta learning and domain adaptation. The goal is to improve rapid adaptation and cross-scenario robustness in dynamic environments. The method uses a shared encoder for representation learning over multiple source transaction features. At the task level, it introduces a meta learning training paradigm. A support set-driven fast update mechanism is used to learn transferable initialization and efficient parameter adjustment. This enables the model to form an effective decision boundary quickly under limited target domain labels. To mitigate distribution shift caused by channel switching, temporal drift, and changes in business strategies, the framework adds domain alignment constraints at the representation level. It learns domain-invariant risk features by minimizing the discrepancy between source and target latent representations. This systematically presents stable ranges and behavioral patterns under different risk bias settings and scenario changes. The study provides a technical solution for credit fraud detection in open environments that supports unified modeling, fast adaptation, and cross-domain robustness.

Credit fraud detection is a central problem in financial risk management. It is challenging due to complex relationships among transaction entities, strong coordination of fraudulent activities, and high levels of behavioral camouflage at the individual level. Traditional approaches mainly rely on features from independent samples. They often fail to represent multi-entity interaction structures effectively. From a relational modeling perspective, this study investigates a graph neural network-based method for credit fraud detection. The method represents transaction entities and their interactions as a unified graph. It performs information propagation and neighborhood aggregation in graph space. It jointly models node attributes and structural context. It therefore learns more discriminative risk representations. During modeling, the network captures both local interaction patterns and multi-hop relational information. This makes anomalous patterns embedded in complex transaction networks more explicit. Based on the proposed method, this study establishes a standardized comparative evaluation on a public credit fraud dataset. It systematically compares the method with multiple existing models. The overall results confirm the effectiveness of graph-structured modeling for credit fraud detection. The findings show that, compared with detection methods that ignore or weaken relational information, graph neural network models achieve clear advantages in stability and consistency of risk identification. They better reflect the propagation characteristics of fraudulent behavior within network structures. These results support modeling credit fraud detection as a relation-aware graph learning problem. This formulation enhances risk characterization in complex financial scenarios. It also provides a structured modeling perspective for building intelligent risk control systems.

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 proposes a knowledge-graph-guided generative detection model to address the challenges of complex feature associations, hidden behavioral patterns, and sparse semantic information in financial fraud detection. The study maps multi-source heterogeneous data, such as accounts, transactions, devices, and geographic information, into a knowledge graph structure to model semantic dependencies and behavioral relationships among financial entities at both node and relation levels. Based on this, the model introduces a relation-aware encoder to obtain high-dimensional structural embeddings and employs a generative reasoning mechanism to learn the distribution of latent fraud patterns, enabling semantic identification of complex risk behaviors without relying on fixed rules. During generation, the method integrates knowledge constraints and structural priors, allowing the model to reconstruct potential fraud chains from a global semantic perspective and significantly enhance interpretability and generalization. Validation on real transaction datasets shows that the model outperforms comparison methods in AUC, ACC, Precision, and Recall, demonstrating the effectiveness of the knowledge-enhanced generative framework in capturing cross-entity dependencies and dynamic risk relationships. Overall, the proposed knowledge-graph-guided generative detection model achieves a unified framework of structural modeling, semantic reasoning, and anomaly generation, providing a new systematic solution for the intelligent detection of complex financial fraud behaviors. CCS CONCEPTS: Computing methodologies~Machine learning~Machine learning approaches.