AI and Data Analytics in Sustainable Financial Reporting and ESG Disclosure: A Systematic Literature Review

Abstract

This research investigates the growing role of artificial intelligence and data analytics in sustainable financial reporting and Environmental, Social, and Governance (ESG) disclosure. Drawing on a systematic review of 45 peer-reviewed articles, the study identifies four research areas: natural language processing for disclosure analysis, machine learning for ESG evaluation, AI-enabled assurance, and digital reporting frameworks. The findings indicate that AI can improve reporting efficiency, data verification, and transparency while introducing challenges related to algorithmic bias, accountability, and regulatory oversight.

Introduction

Increasing sustainability disclosure requirements and stakeholder expectations have accelerated the adoption of digital technologies in corporate ESG reporting. Regulatory frameworks, including the Corporate Sustainability Reporting Directive (CSRD) and International Sustainability Standards Board (ISSB) standards, have intensified the need for reliable, comparable, and verifiable sustainability information.

The research examines how artificial intelligence and advanced analytics are addressing these requirements. It highlights the fragmented nature of existing studies across accounting, auditing, machine learning, and sustainability management, emphasizing the need for an integrated understanding of AI applications in ESG disclosure.

Research Objectives and Methodology

The study aims to assess how AI and data analytics influence sustainability reporting, identify major research trends, evaluate technological applications, and examine opportunities for improving ESG disclosure quality and assurance.

The researchers conducted a systematic literature review using the PRISMA 2020 framework, analyzing 45 peer-reviewed articles from the Scopus database published between 2020 and 2025. Bibliometric mapping through VOSviewer and qualitative thematic analysis were used to identify research patterns and applications. The study also developed a classification framework distinguishing AI used as a primary research method from AI used as analytical support.

Implications and Applications

The findings demonstrate the potential of AI-driven technologies to strengthen sustainability reporting and ESG verification. Natural language processing can support disclosure analysis and greenwashing detection, while machine learning can improve ESG evaluation and identify reporting patterns. AI-enabled auditing and external data verification can also strengthen the reliability of sustainability information.

For businesses, regulators, assurance providers, and investors, the research emphasizes integrating AI with effective data governance, human oversight, and regulatory compliance processes. Organizations should prioritize transparency, explainable algorithms, and appropriate verification controls to ensure that technological adoption produces meaningful improvements in ESG accountability.

Conclusion

The research concludes that artificial intelligence and data analytics are increasingly important in the transition toward data-driven, transparent, and verifiable sustainability reporting. Applications involving disclosure analysis, ESG scoring, automated assurance, and regulatory compliance offer opportunities to improve the quality and efficiency of ESG information.

However, the effectiveness of these technologies depends on organizational readiness, data quality, and responsible governance. The authors recommend further research into multilingual AI applications, long-term reporting outcomes, ethical assurance practices, and AI adoption across emerging markets.

DOI: 10.3390/su18115393

Original Research Paper Link: https://doi.org/10.3390/su18115393