His study investigates the application of causal representation learning in financial auditing risk identification, aiming to address problems in traditional methods such as spurious correlations, limited interpretability, and unstable recognition. The proposed framework is built around causal-driven latent representations, where nonlinear mapping is used to obtain deep feature representations of financial data, and structural equation models are employed to establish causal dependencies, thereby removing the interference of non-causal features in risk modeling. On this basis, causal regularization constraints are introduced, and the joint optimization of the objective function enhances the consistency and robustness of representations, improving the reliability and interpretability of the model in complex scenarios. Furthermore, in the risk scoring stage, causal representation is combined with intervention effect calculation, which enables risk identification to provide not only outcome judgments but also insights into the underlying driving mechanisms, thereby improving traceability of risk sources. To verify effectiveness, a dataset closely related to financial auditing tasks was constructed, and comparative experiments under an alignment robustness benchmark were conducted. The results show that the proposed method outperforms existing models in ACC, Precision, Recall, and F1-Score, with notable advantages in robustness and interpretability. In addition, hyperparameter sensitivity experiments analyzed the impact of the causal regularization coefficient on model performance, and the results indicate that appropriate causal constraints can significantly improve stability while maintaining predictive accuracy. Overall, the proposed causal representation learning framework enables more precise and reliable risk identification in financial auditing and provides strong support for building intelligent and data-driven auditing systems.

Fraudulent financial reporting continues to undermine market integrity and threaten stakeholder interests globally. Conventional analytical methodologies demonstrate limited effectiveness in recognizing intricate temporal relationships and nuanced irregularities characteristic of accounting manipulation. This research introduces an enhanced recurrent neural architecture incorporating multi-head attention layers with bidirectional LSTM components, specifically engineered for identifying accounting anomalies. Our architectural design combines attention-weighted feature selection with dual-directional temporal processing to simultaneously extract prolonged sequential patterns and pivotal indicators spanning multiple reporting cycles. Through rigorous evaluation using a dataset of 2,500 corporate entities across 12 reporting cycles, our methodology achieves 93.87% classification accuracy, 92.41% positive predictive value, 91.56% sensitivity, and 91.98% harmonic mean of precision-recall. These metrics surpass conventional statistical methods and standard deep architectures by substantial margins. The attention weight distributions offer transparent interpretability, revealing which specific accounting ratios and temporal windows contribute decisively to fraud predictions, thereby enhancing practical deployment feasibility in professional audit environments.

This study proposes an intelligent audit risk assessment method that integrates causal structure modeling, causal identifiability reasoning, and interpretable representation learning to address the lack of transparency in risk identification, the presence of confounded variable relationships, and the limitations of correlation-based inference in complex audit scenarios. The method first constructs a structured causal graph of the audit workflow to formalize the triggering relationships and interaction paths among audit features, and then applies structural equations and identifiability analysis to reveal latent causal dependencies. Based on this foundation, the model generates interpretable feature embeddings through causally constrained representation learning, allowing inference results to map back to the business semantic space along causal paths and enabling visual analysis of risk formation. To validate the effectiveness of the approach, this study conducts comparison experiments, ablation experiments, and multidimensional sensitivity analyses on a public audit dataset, and evaluates the method across model accuracy, interpretability, noise robustness, distributional shifts, and hyperparameter variations. The experimental results show that the method achieves significant improvements over existing models in accuracy, precision, recall, and F1-score, while maintaining stable performance under noise interference, class imbalance, learning rate changes, and latent dimension adjustments. The model also produces clear causal chain explanations that help auditors understand risk sources, identify key process components, and trace potential triggering mechanisms through structured reasoning logic. Overall, this study achieves a deep integration of causal inference and intelligent auditing and provides a complete methodological framework and empirical evidence for building transparent, trustworthy, and highly interpretable audit risk assessment systems.

This study proposes a prediction model based on a dynamic spatiotemporal causal graph neural network to address the challenges of complex dynamic dependencies, strong structural correlations, and ambiguous causal relationships in corporate revenue forecasting. The model constructs a time-varying enterprise association graph, where enterprises are represented as nodes and industry or supply chain relationships as edges. A graph convolutional network is used to extract structural dependency features, while a gated recurrent unit captures temporal evolution patterns, achieving joint modeling of structural and temporal features. On this basis, a causal reasoning mechanism is introduced to model and adjust potential influence paths among enterprises. A learnable causal weight matrix is used to describe the strength of economic transmission, suppress spurious correlations, and strengthen key causal paths. The model also employs multi-scale temporal aggregation and attention fusion mechanisms to dynamically integrate multidimensional information, enhancing adaptability to both long-term trends and short-term fluctuations. Experimental results show that the proposed model outperforms mainstream methods in multiple metrics, including MSE, MAE, MAPE, and RMAE, verifying its effectiveness in capturing corporate revenue dynamics, modeling economic causal dependencies, and improving prediction accuracy. This study establishes a unified framework that integrates spatiotemporal dependency modeling with causal structure reasoning, providing new insights and methodological foundations for intelligent forecasting in complex economic systems.

This study proposes a dynamic graph enhancement framework designed for uncertainty conditions to address structural uncertainty, temporal nonlinearity, and multi-scale dependency in corporate financial health risk modeling, where accurately capturing the dynamic evolution of financial states under uncertainty has become a core scientific problem in the digital transformation of financial intelligence and corporate governance. The method starts from multi-source financial data and first performs multi-scale decomposition and normalization to achieve a hierarchical representation of financial time-series signals. Based on this, a dynamically updated enterprise correlation graph is constructed to capture potential risk transmission relationships among firms and to monitor industrial systematic risk. An uncertainty correction mechanism is then introduced to adaptively enhance the adjacency matrix, effectively mitigating modeling bias caused by data noise and structural perturbations. The main model adopts a time-aware graph convolutional network (T-GCN) to jointly learn temporal dependencies and graph topological features, combined with a contrastive learning constraint to strengthen feature consistency and the robustness of risk representation. Experimental results show that the proposed model significantly outperforms mainstream methods such as Transformer, GNN, GAT, and BiLSTM on multiple corporate financial datasets, achieving an average accuracy of 0.921 and improving the F1-Score by about 3.1%. These results verify its robustness and high-precision risk identification capability under dynamic uncertainty conditions. Overall, this study achieves dynamic modeling and structure-aware optimization of corporate financial health, providing an effective technical approach for financial risk monitoring, health assessment, and scenario analysis for financial forecasting and planning in complex economic environments.