Authors:
Anshor Alfayed Tanjung, Jevon Nainggolan, Iskandar Muda, R. Regin, M. Mohamed Thariq
Addresses:
Department of Accounting, Universitas Sumatera Utara, Medan Campus, Sumatera, Indonesia. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.
Abstract:
The exponential growth of environmental, social, and governance (ESG) reporting has resulted in a bewildering variety of frameworks and mixed data quality. There is a possibility that greenwashing and comparability concerns for stakeholders will arise in the absence of a single auditing standard. This is because firms report on their non-financial performance more frequently. This study presents a novel technology-driven auditing system to integrate multiple global reporting standards into a single compliance metric. In this article, Natural Language Processing and statistical scoring methods are utilised to evaluate the credibility of self-reported environmental, social, and governance (ESG) information against global standards. The assessment is based on a sample of 477 different CSR reports. To identify gaps in governance transparency and social responsibility indicators, the research uses Python-based text-mining algorithms and domestically built scoring matrices. The findings suggest that, despite environmental reporting progressing towards maturity, the social and governance features remain inadequately adapted to subjective interpretation and inconsistency. The proposed method offers a standardised framework for external auditors to assess environmental, social, and governance (ESG) assertions, thereby contributing to greater openness and accountability in the capital market.
Keywords: Human Rights Education; Curriculum Integration; Active Citizenship; Capital Market; Variable Data Quality; Non-Financial Performance; Natural Language Processing; Global Standards.
Received: 16/10/2024, Revised: 07/01/2025, Accepted: 21/03/2025, Published: 09/09/2025
DOI: 10.64091/ATISL.2025.000202
AVE Trends in Intelligent Social Letters, 2025 Vol. 2 No. 3 , Pages: 115-123