Article: Corporate internal control evaluation based on contrastive learning neural networks from the perspective of multi-source data Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.34 pp.36 - 57 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

Title: Corporate internal control evaluation based on contrastive learning neural networks from the perspective of multi-source data

Authors: Jie Wei

Addresses: School of Management, The Open University of Shaanxi, Xi'an, 710000, China

Abstract: 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.

Keywords: internal control evaluation; contrastive learning; multi-source data fusion; neural networks; financial risk prediction.

DOI: 10.1504/IJICT.2026.153000

International Journal of Information and Communication Technology, 2026 Vol.27 No.34, pp.36 - 57

Received: 15 Dec 2025
Accepted: 15 Jan 2026
Published online: 17 Apr 2026 *

Title: Corporate internal control evaluation based on contrastive learning neural networks from the perspective of multi-source data

Keep up-to-date