Leveraging Data Science for Fiscal Governance: Machine Learning Approaches to Taxpayer Segmentation and Risk Profiling

The integration of data science methodologies into fiscal governance represents a paradigm shift in how tax authorities approach taxpayer management and risk assessment. This study explores the application of machine learning algorithms for taxpayer segmentation and risk profiling within contemporary fiscal governance frameworks. Through comprehensive analysis of existing literature and emerging practices, this research investigates how advanced analytical techniques can enhance tax compliance monitoring, optimize resource allocation, and improve overall fiscal policy effectiveness. The research employs a mixed-methods approach, combining quantitative analysis of taxpayer data patterns with qualitative assessment of governance frameworks across multiple jurisdictions. Key findings indicate that machine learning models, particularly clustering algorithms and predictive analytics, significantly improve the accuracy of taxpayer risk assessment compared to traditional rule-based systems. The study reveals that supervised learning techniques achieve classification accuracies exceeding 85% in identifying high-risk taxpayers, while unsupervised learning methods effectively segment taxpayer populations into meaningful behavioral groups. Furthermore, the research demonstrates that data-driven approaches to fiscal governance enable more targeted audit strategies, reducing compliance costs while increasing revenue recovery rates. The implementation of machine learning in taxpayer segmentation reveals distinct behavioral patterns that correlate with compliance probability, payment timeliness, and audit outcomes. These insights facilitate the development of personalized taxpayer engagement strategies and risk-adjusted compliance programs. However, the study also identifies significant challenges in data quality, algorithmic transparency, and regulatory compliance that must be addressed for successful implementation. Privacy concerns and ethical considerations surrounding automated decision-making in tax administration require careful balancing with efficiency gains. The research contributes to the growing body of knowledge on digital governance by providing a comprehensive framework for integrating machine learning technologies into fiscal management systems. The findings have practical implications for tax administrators, policy makers, and technology vendors seeking to modernize public sector analytics capabilities. The study concludes that while machine learning offers substantial benefits for fiscal governance, successful implementation requires strategic alignment of technological capabilities with regulatory requirements and organizational change management processes.