Design and Analysis of an AI-Driven Tax Avoidance Detection System in Big Data Environments for Public Sector Tax Administration

This paper presents a descriptive design and conceptual analysis of an AI-driven Tax Avoidance Detection System (TADS), developed for deployment in big data environments to support tax compliance monitoring within public sector administrations. While tax avoidance remains legally permissible, its widespread and opaque application undermines public revenue generation, exacerbates socio-economic inequality, and diminishes state fiscal capacity. TADS is proposed as a hybrid analytical framework that integrates deterministic rule-based logic with deep learning techniques—specifically Long Short-Term Memory (LSTM) networks—to identify complex, time-evolving patterns of tax avoidance across large-scale financial datasets. The study critically examines the structural limitations of existing tax compliance systems, drawing comparative insights from AI implementations in countries such as Australia, the United Kingdom, and China. It also explores the ethical, technical, and institutional considerations necessary for integrating intelligent detection tools into public governance frameworks. By synthesizing international best practices with a modular and interpretable AI architecture, this paper contributes a policy-oriented system blueprint designed to improve transparency, audit efficiency, and evidence-based enforcement in tax administration. Notably, the scope of this paper is limited to the conceptual and descriptive components of the system which form the basis for future research work.