Multi-objective Optimization and Machine Learning Framework for Tax Risk Management: Theory and Applications

This paper proposes a novel framework for tax risk management that integrates multi-objective optimization with machine learning techniques. Building upon the Tax Control Framework recently introduced in Italy, we develop a comprehensive mathematical model that simultaneously optimizes multiple competing objectives: minimization of tax risk exposure, cost efficiency of control systems, and maximization of operational flexibility. We establish two fundamental theorems: the Pareto Optimality Characterization Theorem for tax risk portfolios and the Convergence Theorem for the proposed hybrid learning algorithm. The framework employs ensemble machine learning methods, including gradient boosting and neural networks, to predict tax risk events and classify risk severity levels. We demonstrate that our multi-objective approach yields superior performance compared to traditional single-objective methods, providing decision-makers with a set of Pareto-optimal solutions that balance competing organizational goals. Numerical experiments on synthetic tax datasets validate the theoretical results and demonstrate the practical applicability of the framework.