This paper addresses key challenges in credit fraud detection, including small sample sizes, imbalanced data distribution, and rapidly changing fraud patterns. A meta-learning-based detection method is proposed to enhance the model's adaptability and detection efficiency in complex scenarios. The method adopts a two-level architecture consisting of a meta-learner and a task-learner. By extracting shared knowledge across multiple fraud detection tasks, the model can rapidly update its parameters. This allows it to maintain high detection performance when facing new types of fraud. In the method design, a task-level gradient update mechanism and a weighted loss strategy are introduced. These components help address the challenges caused by rare fraud samples and class imbalance. The proposed model is systematically evaluated through multiple experiments. These include different task quantities, varying degrees of data imbalance, and injected abnormal fraud patterns. The results confirm the model's effectiveness and robustness under diverse conditions. Experimental findings show that the proposed method outperforms traditional models in accuracy, precision, and recall. It also demonstrates better stability and generalization when facing dynamically changing fraud scenarios. This study highlights the application potential of meta-learning in credit fraud detection. It provides technical support for building more intelligent financial risk control models.

This paper focuses on the risk control challenges arising from behavioral interactions among multiple participants in financial markets. It proposes a financial risk game framework based on multi-agent collaborative modeling. The framework integrates environment state encoding, strategy generation networks, and a coordinated evolution mechanism to dynamically model the propagation paths of systemic risk in complex markets. During the modeling process, each agent generates behavior strategies independently based on local observations. The market state is then updated over time through a system evolution function, which captures the coupling between multi-agent behaviors and the risk structure. To verify the stability and adaptability of the proposed method, a series of sensitivity experiments are designed. These experiments examine the impact of hyperparameters, data characteristics, and environmental disturbances on system performance. The study focuses on several key factors, including the number of agents, time window length, sampling frequency, and anomaly injection. The experimental results show that the method performs well across multiple dimensions such as strategy stability, modeling consistency, and coordination efficiency. The model demonstrates strong structural representation and dynamic adaptability. Through comparative experiments and disturbance analysis, the study further reveals the model's capability to simulate the evolution of financial risk structures under various conditions. This provides a valuable methodological reference for intelligent modeling of complex financial systems.

This paper proposes a deep risk discrimination framework that integrates a Siamese network structure with a federated optimization mechanism to address challenges in financial transaction environments, including multi-source heterogeneity, data silos, and unclear decision boundaries. The method preserves data privacy by deploying structurally symmetric and parameter-sharing Siamese subnetworks across multiple clients. It performs contrastive encoding on transaction sample pairs and uses a contrastive loss function to pull normal samples closer and push anomalous ones apart in the embedding space. To improve the detection of low-frequency, high-impact behaviors, the model incorporates an entropy-aware regularization term and an embedding compression module. These components guide local training to suppress redundant representations and enhance embedding compactness. During the federated optimization process, the classic FedAvg strategy is used to aggregate models from different clients, improving global semantic alignment and generalization. Experiments on real transaction datasets demonstrate the superior performance of the proposed method across multiple metrics. The model shows particularly strong results in embedding consistency and anomaly boundary detection, confirming its modeling value and discriminative power in financial risk control.

This study addresses the challenges of scarce fraudulent samples, complex data distributions, and the limited adaptability of traditional methods in financial statement fraud detection by proposing a self-supervised learning algorithm. The approach first standardizes multidimensional financial indicators to mitigate scale differences, then employs an encoder to construct latent representations that capture high-order nonlinear relationships among indicators. A reconstruction task is introduced as an auxiliary signal, where a decoder approximates the input and minimizes reconstruction error to enhance the fidelity of representations. In parallel, a classification module distinguishes normal from fraudulent statements, with the model jointly optimizing reconstruction and classification losses to improve both feature completeness and discriminative ability. Experiments on a public financial fraud dataset show that the proposed method significantly outperforms existing baselines on Precision, Recall, F1-Score, and AUC, with particular strength in minority class recognition under imbalanced and limited data. Additional sensitivity experiments demonstrate that the method remains stable and robust across variations in optimizer type and imbalance ratios, confirming its effectiveness in complex financial environments. Overall, the algorithm provides an efficient and reliable pathway for fraud detection and exhibits distinctive advantages in accuracy and adaptability.