(untitled) (gjstudies, 2021)
This research introduces a novel, cross-disciplinary application of machine learningto enhance the quality and scope of environmental assurance audits. Moving beyondtraditional financial audit applications, we develop and validate a hybrid methodologythat integrates unsupervised anomaly detection with supervised classification modelsto identify material misstatements and compliance failures in corporate environmental disclosures. The core innovation lies in the formulation of the audit process as amulti-modal data fusion problem, where structured financial data, unstructured textual disclosures from sustainability reports, and time-series environmental performancemetrics are jointly analyzed. We propose a two-stage framework: first, a graph-basedanomaly detection algorithm identifies high-risk entities and unusual reporting patternsacross 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 modelson a unique, hand-collected dataset of 450 corporate sustainability reports from 2000 to2004, paired with verified regulatory compliance outcomes. Results demonstrate thatthe proposed hybrid model achieves a 94.7% accuracy in predicting subsequent regulatory sanctions for environmental misreporting, significantly outperforming baselinelogistic regression (78.2%) and standard audit risk models (81.5%). Furthermore, theanomaly detection stage successfully surfaces non-obvious, systemic reporting biaseswithin specific industries, a finding previously obscured by conventional sample-basedaudit techniques. This work provides a foundational, computational methodology for’Assurance Analytics,’ offering audit firms and regulators a scalable, evidence-basedtool to improve the rigor and reliability of environmental, social, and governance (ESG)assurance, thereby addressing a critical gap in sustainable finance and corporate accountability.