Enterprise AI Analysis
The Role of Artificial Intelligence in Enhancing ESG Disclosure Quality in Accounting
This review synthesizes interdisciplinary insights from accounting, finance, and computational linguistics on artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), as a transformative force in enhancing ESG disclosure quality. It delineates ESG disclosure quality across four operational dimensions: readability, comparability, informativeness, and credibility. By integrating cutting-edge methodological innovations, empirical linkages, and normative discussions, it demonstrates AI's efficacy in scaling measurement, harmonizing heterogeneous narratives, and prototyping greenwashing detection. The review proposes a forward-looking agenda prioritizing cross-lingual benchmarking, curated greenwashing datasets, AI-assurance pilots, and interpretability standards to harness AI for substantive, equitable improvements in ESG reporting and accountability.
Authors: Jiacheng Liu, Ye Yuan, Zhelun Zhu • Publication: J. Risk Financial Manag. 2026, 19, 58
Executive Impact: Key Findings at a Glance
AI is revolutionizing ESG disclosure quality, offering unprecedented capabilities across readability, comparability, informativeness, and credibility. Here’s a snapshot of its quantifiable impact.
0
Accuracy in ESG Insight Extraction
0
Governance Disclosure Contribution to Rating Variance
0
Regulatory Compliance for Feature Explanations
0
Performance Degradation in Cross-Lingual Applications
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Readability
Comparability
Informativeness
Credibility
Readability
AI tools capture semantic clarity beyond surface metrics, improving cognitive accessibility. However, there's a risk of rhetorical polish over substance, especially with generative AI.
~10,000
Chinese Sustainability Reports Analyzed for Readability
Quote (Bonsall et al., 2017): "Readability improvements are often concentrated in sections of reports that serve a marketing function, such as executive summaries, rather than in sections containing material quantitative indicators."
Comparability
AI enhances comparability by mapping unstructured disclosures to structured categories (e.g., SASB, ISSB), enabling large-scale benchmarking. Limitations include taxonomy biases and English-centric training.
Enterprise Process Flow for Comparability
Unstructured ESG Disclosures
→
AI-powered NLP & ML
→
Structured, Comparable Data
→
Enhanced Benchmarking & Analysis
| AI Method | Benefit | Limitation |
|---|---|---|
| Topic Classification (BERT, FinBERT) |
|
|
| Contextual embeddings, transformer models |
|
|
Informativeness
AI extracts predictive signals and filters immaterial content. Its effectiveness relies on materiality alignment, corroboration with hard metrics, and institutional context.
Since 2023
Year of Climate Exposure Measure Development
Quote (Calamai et al., 2025): "Narrative claims become more informative when supported by quantitative data."
Credibility
AI offers tools for greenwashing detection, cross-modal validation, and anomaly detection. Challenges include lack of labeled datasets, interpretability issues, and risk of algorithmic greenwashing.
IFC's MALENA Platform
The International Finance Corporation's (2024) MALENA platform utilizes NLP to analyze ESG documents in multiple languages and identify risk terms with context-dependent sentiment analysis. This system addresses Anglo-centric bias and shows comparable performance across linguistic contexts, though formal benchmarking studies are limited.
Highlight: Addresses Anglo-centric bias with multilingual analysis.
Quote (Calamai et al., 2025): "Without high-quality labeled datasets, supervised models risk overfitting or misclassification."
Advanced ROI Calculator: Quantify Your ESG AI Impact
Estimate the potential savings and reclaimed hours for your organization by leveraging AI in ESG disclosure processes.
Implementation Roadmap: Your Path to Enhanced ESG Disclosure
A structured approach ensures successful integration and maximum impact of AI in your ESG reporting strategy.
AI-Driven Data Extraction
Automated processing of unstructured ESG narratives to extract key data points and identify patterns.
Standardized Reporting Frameworks
Mapping diverse disclosures to common taxonomies (SASB, ISSB) for enhanced comparability.
Greenwashing Detection & Risk Assessment
Flagging inconsistencies and potentially misleading statements by cross-validating narrative claims with performance data.
Enhanced Stakeholder Communication
Generating clear, concise, and credible reports tailored to different audience needs.
Continuous Monitoring & Assurance
Real-time tracking of ESG performance and disclosure quality, supporting internal and external audits.
Ready to Transform Your ESG Disclosure?
Speak with our AI specialists to tailor a solution that meets your unique needs and drives tangible results.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat