AUDAN: An adversarial unsupervised domain adaptation network informed decision-making in audit risk assessment

This paper develops an adversarial unsupervised domain adaptation network (AUDAN) for audit risk assessment from a data-driven perspective, which can offer a novel solution for the audit issue data distribution shift problem in audits. The proposed AUDAN consists of three neural network–based modules: the feature extractor, the domain classifier, and the risk-level classifier. A joint adversarial learning scheme based on these three modules is developed to enable learning discriminative and domain-invariant feature representations from audit issue data. In developing the AUDAN, the data is reorganized into source and target domains. A simple yet effective model selection technique, called latest held-out source risk validation, is proposed for the time-variant domain shift, where the data distribution of one period is closer to the data distribution of the adjacent period. The superiority of the latest held-out source risk validation technique has been theoretically justified. Computational experiments based on a real-world dataset were performed to verify the advantages and effectiveness of the AUDAN. The results showed that the AUDAN achieves superior testing performance in most cases. Additionally, the AUDAN yields robust classification results and outperforms the benchmarking models, including the train-on-target model, which is trained with the label information of target domain data revealed and can be regarded as a strong competitor. Ablation studies further showed the superiority of the developed latest held-out source risk validation method. © 2025 The Author(s).