Severity-Based Prioritisation of Administrative Tax Non-Compliance: A Multiclass Prediction Approach Using Financial and Governance Data

Abstract

Tax authorities face growing volumes of filings and payments and must manage procedural non-compliance (e.g., late filing, late payment and the accumulation of tax arrears) with limited administrative capacity. Many existing machine learning (ML) and artificial intelligence (AI) applications in tax administration rely on binary outcomes, which limits severity-based prioritisation and the targeting of low-cost interventions. This study develops a multiclass prediction model for administrative tax compliance severity using Slovakia's public tax reliability index, which classifies companies into three categories based on regulator-defined administrative criteria. Using only financial statement ratios and governance indicators, we evaluate nine classifiers and five resampling techniques for class imbalance. Gradient boosting models (XGBoost and CatBoost) perform best, reaching an OvR AUC-ROC above 96% for 1-year forecasts, with modest declines for 2- and 3-year horizons. SHAP explanations indicate that smaller boards and indicators consistent with liquidity constraints and tax-payment pressure are associated with higher-severity administrative classes. The proposed workflow offers a transferable framework for multiclass, long-horizon compliance risk prediction and can support proactive case management (e.g., targeted reminders and payment facilitation, including payment plans, and debt prioritisation) in advance of the regulator's semi-annual updates; it may also provide researchers with a potential early-warning label of administrative compliance frictions that could be examined in relation to financial distress.