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

The intensification of ESG disclosure requirements under the Corporate Sustainability Reporting Directive (CSRD) and the International Sustainability Standards Board (ISSB) has increased the demand for artificial intelligence (AI) and data analytics to support large-scale sustainability reporting and verification. However, the existing academic literature remains fragmented across disciplinary domains, including natural language processing, machine learning, auditing, and regulatory compliance. This study addresses this gap through a PRISMA 2020-compliant systematic literature review of 45 peer-reviewed articles published between 2020 and 2025 and indexed in the Scopus database. The analysis combines bibliometric techniques using VOSviewer with qualitative thematic content analysis. The results reveal a rapidly expanding research field with a compound annual growth rate of 91.9%. Four major thematic dimensions emerge: (i) NLP and text mining for ESG disclosure analysis; (ii) machine learning applications for ESG scoring and corporate performance; (iii) AI-enabled ESG assurance, auditing, and governance; and (iv) regulatory frameworks and the digital transformation of sustainability reporting. The findings indicate that AI technologies are progressively transforming ESG disclosure from a predominantly narrative and self-reported practice into a data-driven and verifiable transparency system. These developments have important implications for regulators, corporate practitioners, assurance providers, and investors seeking to enhance the reliability and comparability of sustainability disclosures.