Semantic Analysis of ESG Disclosures and Its Impact on Corporate Credit Risk Assessment

Authors

  • Gaurav Ranguly Department of Computer Science, Binghamton University, Binghamton, NY, USA.
  • Troy A. Lindberg Department of Computer Science, University of Central Florida, Orlando, FL, USA.

Keywords:

ESG disclosures; semantic analysis; credit risk assessment; natural language processing; explainable artificial intelligence; financial governance

Abstract

The expansion of environmental, social, and governance disclosures has produced a substantial and heterogeneous textual evidence base for credit risk assessment. Unlike standardized financial statements, ESG narratives are shaped by managerial framing, sectoral expectations, and regulatory influences, creating both opportunities and interpretive challenges. This paper examines the semantic analysis of ESG disclosures and its implications for corporate credit risk assessment from a systems perspective. It treats semantic analytics as a socio-technical infrastructure that encompasses data acquisition, natural language processing, model governance, institutional deployment, and auditability rather than as a narrow modeling exercise. The paper discusses how semantically derived signals can reveal risk-relevant properties of corporate disclosures, including ambiguity, inconsistency, selective emphasis, and temporal deviation from prior reporting. These signals may complement conventional credit models by capturing dimensions of creditworthiness that are not fully reflected in accounting ratios or aggregated ESG ratings. The analysis emphasizes structural trade-offs among predictive richness, explainability, fairness, and operational resilience. It also addresses policy concerns related to disclosure standardization, model validation, issuer asymmetries, and the sustainability of computational infrastructure. The paper concludes that semantic ESG analytics can strengthen credit risk assessment only when embedded within robust governance arrangements that align analytical opacity with institutional accountability.

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Published

2026-08-22

How to Cite

Gaurav Ranguly, & Troy A. Lindberg. (2026). Semantic Analysis of ESG Disclosures and Its Impact on Corporate Credit Risk Assessment. Global Financial Analytics Research Review, 1(1). Retrieved from https://www.gfarr.org/index.php/home/article/view/150

Issue

Vol. 1 No. 1 (2026): Global Financial Analytics Research Review

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Articles

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