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 targets key challenges in enterprise financial anomaly monitoring, including coupled multi-source heterogeneous features, concealed anomaly signals, and cost-sensitive alerting. It proposes a unified monitoring framework based on representation-driven data modeling. The approach first normalizes and encodes transaction and identity-related fields within a unified feature space. A learnable representation mapping function is then used to obtain low-dimensional latent representations. A reconstruction consistency mechanism is introduced to suppress noise while preserving essential behavioral information. In the latent space, normal behavior structures are statistically characterized. Structural anomaly signals are derived by measuring the deviation between representation centers and sample representations. These signals are combined with reconstruction errors from the original inputs to form a unified anomaly score. This enables continuous ranking of samples and threshold-based anomaly decisions. To ensure controllability in training and deployment, the framework is systematically analyzed from the perspectives of hyperparameter sensitivity, including learning rate, optimizer choice, and network depth, as well as environmental sensitivity under input noise injection. The analysis delineates the impact boundaries of key configurations on overall detection performance and stability. The results demonstrate that the framework maintains consistent risk identification characteristics under different configurations and perturbations. It provides sortable and configurable anomaly signals for enterprise risk alerts, audit sampling, and compliance inspection.

As the complexity and volatility of global financial markets increase, traditional market forecasting and risk control methods can no longer effectively cope with the various uncertainties in the modern financial environment. Therefore, financial market prediction and risk control based on deep reinforcement learning has become a research hotspot. This paper proposes a market turbulence prediction and risk control model based on the improved A3C algorithm, aiming to improve the accuracy of market volatility prediction and the precision of risk control through reinforcement learning methods. Through experimental verification on multiple public financial data sets, the model proposed in this paper is superior to traditional reinforcement learning algorithms such as Q-learning, DQN, and PPO in terms of prediction accuracy, risk control accuracy, and convergence speed, showing strong learning ability and adaptability. In terms of model design, the parallel mechanism and advantage function are combined to significantly improve the training efficiency and model stability. Experimental results show that the model proposed in this paper can not only effectively capture the patterns of market turbulence but also adjust strategies according to real-time market data, thereby achieving effective control of risks. In addition, this paper also analyzes the contribution of each module to the model performance through ablation experiments, further verifying the effectiveness and advantages of the model. Finally, this paper looks forward to future research on market forecasting and risk control technology based on deep reinforcement learning. It is believed that with the continuous advancement of technology, AI-based financial risk management will play an increasingly important role in practical applications. It has become a powerful tool to enhance the stability of financial markets and respond to unexpected market risks.

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.

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.