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https://www.igi-global.com/gateway/chapter/376902 Tax evasion poses significant challenges to governments, undermining revenue collection and trust in public institutions. E-Government platforms, leveraging advanced digital tools and data analytics, offer a promising solution to address this issue by enabling efficient monitoring and profiling of taxpayer behavior. This study explores the role of E-Government systems in mitigating tax evasion through the behavioral profiling of non-compliant taxpayers. By integrating machine learning algorithms and anomaly detection techniques, the research identifies patterns of non-compliance and develops predictive models for early detection of evasion risks. Furthermore, the study examines the psychological and socio-economic factors influencing taxpayer behavior, emphasizing the role of trust, transparency, and system usability in fostering voluntary compliance. The findings underscore the importance of data-driven policy interventions and ethical considerations in behavioral profiling, offering a robust framework for enhancing tax compliance and promoting fiscal transparency.
Written by mohammedyounusghazni
Contributor at BSMe2e β’ Passion Projects | Smart Cities
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