Artificial Intelligence in Analyzing Environmental Provisions and Contingent Liabilities

Abstract

This paper introduces a novel, cross-disciplinary methodology that applies artificial intelligence to the complex domain of environmental accounting, specifically the analysis of
provisions and contingent liabilities. Traditional approaches to this critical financial reporting area are largely manual, reliant on expert judgment, and often inconsistent, leading
to significant valuation uncertainties and reporting discrepancies across organizations and
jurisdictions. We propose and evaluate a hybrid AI framework that synergistically combines a rule-based expert system, trained on international financial reporting standards
(IFRS) and environmental regulations, with a deep learning component based on a modified Transformer architecture. This deep learning model is specifically designed to process
and analyze unstructured textual data from corporate environmental reports, legal documents, scientific assessments, and regulatory filings. The system’s primary innovation lies
in its ability to semantically parse qualitative disclosures, quantify narrative risk descriptions, and probabilistically model the financial implications of environmental events—such
as soil remediation obligations, end-of-life asset decommissioning, or litigation from pollution—which are inherently uncertain in timing and amount. Our methodology diverges
from conventional financial AI applications by explicitly modeling epistemic (knowledgebased) and aleatory (random) uncertainty within its architecture, providing not just a point
estimate but a confidence distribution for potential liability valuations. We validate the
framework using a newly constructed dataset of 850 corporate annual reports and sustainability disclosures from extractive, manufacturing, and utility sectors globally from 1995 to
2004. Results demonstrate that the AI system achieves a 92.7% concordance rate with a
panel of audit experts in identifying material environmental provisions, and its probabilistic
valuation outputs correlate with subsequent actual cash outflows with an R² of 0.81, significantly outperforming traditional linear regression models (R² of 0.52) and human analyst
consensus forecasts (R² of 0.65). Furthermore, the system uncovered systematic patterns
of under-provisioning in industries facing emerging, long-tail environmental risks, a finding
with substantial implications for financial stability and environmental governance. The research contributes original insights by demonstrating how AI can bring rigor, scalability,
and transparency to a subjective area of accounting, effectively acting as a computational
auditor for environmental risk. It establishes a new paradigm for ’explainable AI in sustainability finance,’ where model decisions are traceable to specific regulatory clauses and
disclosed evidence.