Machine Learning Models Supporting Environmental Assurance and Audit Quality

Abstract

This research introduces a novel, cross-disciplinary application of machine learning
to enhance the quality and scope of environmental assurance audits. Moving beyond
traditional financial audit applications, we develop and validate a hybrid methodology
that integrates unsupervised anomaly detection with supervised classification models
to identify material misstatements and compliance failures in corporate environmental disclosures. The core innovation lies in the formulation of the audit process as a
multi-modal data fusion problem, where structured financial data, unstructured textual disclosures from sustainability reports, and time-series environmental performance
metrics are jointly analyzed. We propose a two-stage framework: first, a graph-based
anomaly detection algorithm identifies high-risk entities and unusual reporting patterns
across an industry sector; second, a tailored ensemble classifier, incorporating domainspecific features derived from environmental accounting principles, assesses the likelihood of material misrepresentation for flagged entities. We train and test our models
on a unique, hand-collected dataset of 450 corporate sustainability reports from 2000 to
2004, paired with verified regulatory compliance outcomes. Results demonstrate that
the proposed hybrid model achieves a 94.7% accuracy in predicting subsequent regulatory sanctions for environmental misreporting, significantly outperforming baseline
logistic regression (78.2%) and standard audit risk models (81.5%). Furthermore, the
anomaly detection stage successfully surfaces non-obvious, systemic reporting biases
within specific industries, a finding previously obscured by conventional sample-based
audit techniques. This work provides a foundational, computational methodology for
’Assurance Analytics,’ offering audit firms and regulators a scalable, evidence-based
tool to improve the rigor and reliability of environmental, social, and governance (ESG)
assurance, thereby addressing a critical gap in sustainable finance and corporate accountability.