A Machine Learning Approach to Audit Modification Risk Prediction in Financial Reporting: Methods, Data, and Human-Centered Challenges

Financial reporting irregularities and audit modifications represent important warning signals of elevated fraud and financial distress risk. While recent studies report high predictive accuracy in fraud detection, most approaches frame the problem as a purely algorithmic classification task and offer limited interpretability for auditors, regulators, and decision-makers. This study reframes financial statement analysis as a human-interpretable audit modification risk prediction problem. It integrates domain-informed feature engineering with machine learning models. Using firm-level financial data and audit disclosures, audit opinions are used as a proxy indicator of elevated fraud-related reporting risk rather than confirmed fraudulent behavior. Logistic Regression, Random Forest, and Gradient Boosting models are trained under class imbalance using cost-sensitive learning and evaluated with recall, ROC–AUC, precision, F1-score, and accuracy. The results demonstrate that humanized categorical representations preserve predictive performance while substantially enhancing interpretability. Permutation-based feature importance analysis further identifies financially intuitive risk patterns and threshold-like conditions associated with elevated audit modification risk. The findings suggest that interpretable, risk-oriented machine learning frameworks can support more transparent and actionable financial reporting risk monitoring systems. Beyond predictive performance, the study discusses human-centered challenges related to model interpretability, decision support, and the integration of machine-learning systems into real-world financial reporting and audit-risk assessment workflows.