Machine Learning Approaches to Assessing Environmental Audit Evidence Quality

Abstract

This research introduces a novel, cross-disciplinary application of machine learning to
a domain traditionally governed by qualitative, expert-driven judgment: the assessment
of evidence quality in environmental audits. While prior work has applied computational
techniques to financial auditing, the unique, heterogeneous, and often unstructured nature
of environmental evidence—encompassing sensor data, satellite imagery, regulatory correspondence, and site inspection reports—presents a distinct and underexplored challenge.
This paper formulates the problem of evidence quality assessment not as a binary classification task, but as a multi-dimensional regression and anomaly detection problem, capturing
the continuous and context-dependent nature of audit assurance. We propose a hybrid
methodology, the Hierarchical Evidence Quality Network (HEQ-Net), which synergistically
combines a Graph Neural Network (GNN) to model the complex relational structure between evidence items (e.g., corroboration, lineage, and conflict) with a Transformer-based
encoder for processing the textual and numerical content of individual evidence documents.
This architecture is trained on a purpose-built corpus of simulated audit engagements, designed with domain experts to reflect realistic evidentiary patterns and quality gradients.
Our results demonstrate that HEQ-Net significantly outperforms conventional natural language processing baselines and expert heuristics in predicting quality scores aligned with
senior auditor judgments (R² = 0.87). More importantly, the model uncovers non-intuitive,
latent features indicative of quality, such as specific temporal patterns in data submission
and subtle linguistic markers in descriptive text that are frequently overlooked in manual review. The findings challenge the prevailing audit paradigm by demonstrating that machine
learning can move beyond automation of routine tasks to provide substantive, analytical
insights into evidence evaluation, thereby enhancing the reliability and efficiency of environmental assurance. This work establishes a new research direction at the intersection of
computational sustainability and audit science.