Causal Representation Learning for Robust and Interpretable Audit Risk Identification in Financial Systems

Jingjing Li (), Qingmiao Gan (), Ruibo Wu (), Chen Chen (), Ruoyi Fang () and Jianlin Lai ()
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Jingjing Li: University of Illinois Urbana-Champaign
Qingmiao Gan: Trine University
Ruibo Wu: University of California
Chen Chen: Vanderbilt University
Ruoyi Fang: Golden Gate University
Jianlin Lai: Babson College

A chapter in Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025), 2026, pp 454-464 from Springer

Abstract: Abstract This 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.

Keywords: Causal representation learning; financial systems; audit risk identification; robustness (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-602-9_40

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