An intelligent tax administration framework integrates data standardization, automated workflows, and dynamic risk modeling to enhance fraud detection in digital environments. By combining machine learning, graph analytics, and platform interoperability, the system modernizes tax governance and strengthens real-time compliance monitoring across high-volume government systems.
-- Traditional tax administration faces critical challenges as fixed audit rules no longer meet fraud identification needs in digitized environments. Recent research establishes comprehensive frameworks integrating intelligent management with anti-fraud applications, addressing inconsistent data standards, fragmented process collaboration, lagging risk models, and platform interoperability barriers. The work proposes optimization strategies establishing unified data standards, promoting automated cross-departmental workflows, enhancing risk detection through dynamic model deployment, and building integrated governance platforms enabling seamless data circulation.
As detailed in the published study “Research on the Combination of Intelligent Management of Tax Data and Anti-Fraud Technology” in Strategic Management Insights, the research constructs intelligent management frameworks spanning data collection automation, data fusion standardization, model algorithm intelligence, and platform integration. Anti-fraud technology operates through a five-stage framework: data perception, feature extraction, risk modeling utilizing clustering and graph algorithms, warning triggering, and coordinated disposal. Optimization strategies address integration challenges through unified data standardization, cross-departmental collaboration, dynamic model deployment, and integrated platform operation.
Practical implementation of intelligent tax data management is reflected in the construction of integrated technology systems that include data collection automation, data fusion standardization, model algorithm intelligence, and platform operation integration. In practical operational contexts, the system enables real-time data capture through multi-source integration, applies intelligent algorithm models such as machine learning and graph computing to identify suspicious patterns, and generates preliminary risk warnings from large-scale data. To address practical integration challenges, the study proposes establishing unified and fine-grained data standards, promoting cross-departmental collaboration through automated workflow integration, enhancing the responsiveness of risk detection models through dynamic model deployment, and building an integrated tax governance platform that enables seamless data circulation and real-time communication across systems.
Contributing to this work is Qifeng Hu, Implementation Consultant Team Lead at the Illinois Department of Revenue, holding a Bachelor of Science in Electrical Engineering from the University of Illinois at Urbana-Champaign. His technical expertise encompasses C#, VB.NET, and SQL. Professional experience spans leading Illinois statewide tax platform development, including IRS Direct File for the state of Illinois last year, implementing fraud-prevention systems for Finnish Tax Administration, and designing excise tax modernization for Michigan Department of Treasury. Research contributions include publications in Strategic Management Insights on intelligent tax data management and anti-fraud technology.
Current leadership as Implementation Consultant Team Lead at the Illinois Department of Revenue encompasses e-services domain ownership for statewide mandatory online tax platforms. Responsibilities include serving as e-services lead for the MyTax Illinois primary taxpayer portal and supervising MyDeveloperPortal development for the Tobacco Tax Uniformity program. Prior contribution includes leading Illinois IRS Direct File implementation for tax year 2024 and implementing a tokenized, encrypted payment infrastructure for the Illinois Liquor Control Commission while mentoring his development team.
The integration of intelligent data management research with government system implementation demonstrates effective pathways to tax administration modernization. By establishing systematic solutions addressing data standardization, process automation, dynamic model deployment, and platform integration, this work removes fundamental barriers to fraud detection effectiveness in high-volume compliance-critical environments. The research-to-implementation methodology supports the development of resilient data-driven tax governance systems while establishing technical foundations enabling secure federal-state interoperability.
Contact Info:
Name: Qifeng Hu
Email: Send Email
Organization: Qifeng Hu
Website: https://scholar.google.ca/citations?user=K6oVzTwAAAAJ&hl=en&authuser=1
Release ID: 89184253
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