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.

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 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.