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 paper addresses complex decision-making scenarios characterized by high uncertainty and high-cost errors, researching a risk-sensitive decision-oriented reinforcement learning mining method. It focuses on resolving the reliability issues arising from tail instability in the reward distribution and out-of-distribution actions under offline data conditions. Methodologically, the decision-making process is modeled using a Markov framework, with the reward distribution as the learning object to retain value information under adverse conditions. Based on this, a conditional risk-value metric is introduced to explicitly characterize and suppress tail risk, ensuring that policy optimization no longer relies solely on expected returns. To mitigate estimation bias and over-extrapolation in offline learning, conservative constraints based on behavioral distribution are further incorporated. By limiting the deviation between the policy and the implicit behavioral distribution in the data, out-of-distribution action expansion is suppressed, and the controllability of policy updates is improved. The overall framework unifies risk measurement and conservative learning into a single optimization form, forming a policy learning mechanism that balances returns and safety. Comparative experimental results show that this method exhibits superior overall performance in terms of average returns, tail reward robustness, and safety-related indicators, validating the effectiveness of the co-modeling of risk-sensitive objectives and conservative constraints, and providing an auditable and adjustable risk control approach for highly reliable intelligent decision-making systems.

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.

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.