Enhancing Corporate Transparency: AI-Based Detection of Financial Misstatements in Korean Firms Using NearMiss Sampling and Explainable Models

Abstract

Corporate transparency is vital for sustainable governance. However, detecting financial misstatements remains challenging due to their rarity and resulting class imbalance. Using financial statement data from Korean firms, this study develops an integrated AI framework that evaluates the joint effects of sampling strategy, model choice, and interpretability. Across multiple imbalance ratios, NearMiss undersampling consistently outperforms random undersampling—particularly in recall and F1-score—showing that careful data balancing can yield greater improvements than algorithmic complexity alone. To ensure interpretability rests on reliable predictions, we apply Shapley Additive Explanations (SHAP) and Permutation Feature Importance (PFI) only to high-performing models. Logistic regression emphasizes globally influential operating and financing accounts, whereas Random Forest identifies context-dependent patterns such as ownership structure and discretionary spending. Even with a reduced feature set identified by explainable AI, models maintain robust detection performance under low imbalance, highlighting the practical value of interpretability in building simpler and more transparent systems. By combining predictive accuracy with transparency, this study contributes to trustworthy misstatement detection tools that reinforce investor confidence, strengthen responsible corporate governance, and reduce information asymmetry. In doing so, it advances the United Nations Sustainable Development Goal 16 (Peace, Justice, and Strong Institutions) by supporting fair, accountable, and sustainable economic systems.