Traditional approaches to assessing corporate internal control effectiveness often depend on isolated data sources or simplistic fusion methods, resulting in limited accuracy and generalisability.To address this, this paper introduces a multi-view contrastive learning network framework that integrates financial statements, managerial narrative disclosures, and market sentiment data as complementary views of a firm's underlying risk profile.The model first learns aligned, robust representations across modalities through self-supervised intra-view and cross-view contrastive pre-training.It then fine-tunes on labelled data with an attention-based fusion mechanism for internal control weakness classification.Experiments show that the proposed framework achieves an area under the curve of 0.842 and a precision of 0.502, exceeding the best baseline by 2.9 percentage points in area under the curve and 10.3% in precision.These results demonstrate that the multi-view contrastive learning network framework significantly enhances the performance, robustness, and interpretability of automated internal control evaluation.
Jie Wei (Thu,) studied this question.