Detecting SME Sales/Use-Tax Compliance Risk with Explainable Gradient Boosting: Evidence from Midwestern Retailers

The small and medium-sized enterprises (SMEs) are significant elements of the national economy and may struggle to pay taxes owing to lack of knowledge, resources, and the aspect of the informal sector. This study produces the pioneer model of interpretability sales and uses tax enforcement among SMEs; it incorporates the conglomerate of gradient boosting techniques and SHAP clarifying. The model can address the problem of data governance and label sparsity based on registry information, POS summative, seasonality, and audit findings and provides equities in the SME cases. Findings indicate that prediction is better than regression and forest baselines, with seasonality, sales anomaly, and registry mismatch being significant risk factors. Local SHAPs and global SHAPs deliver actionable and understandable insights that may be utilized in order to obtain improved transparency and faith over enforcement. Intercounty stability tests revealed that it is strong, and there are small sectoral differences in the Midwest. The results are important in the value of interpretability in the ML systems in promoting tax administration due to the introduction of the appropriate balance between predictive and accountability measures as well as pointing towards a scalable method of digital tax systems and cooperative compliance program implementation.