Abstract
Nowadays, there are multiple actions to fight against tax frauds. While some articles aim to optimize fraud detection, others focus on recovery but there is no overall approach, to the author’s knowledge. This partly explains why fraudsters still flourish and government actions prove ineffective. This chapter presents a comprehensive approach to struggle against tax fraud and optimizing e-Government. The proposal consists in introducing a holistic four-step framework composed that ensures continuous improvement. First, data held by the Tax Administration is reorganized and linked together to enable detection of cross-domain frauds. Then, a model is proposed to improve fraud detection by taking advantage of the previous organization of data. Subsequently, a new stage of resource optimization is proposed to take into account the skills and workload of tax auditors. Finally, a method for comparing and analyzing the results of fraud detection is detailed to ensure a continuous improvement of the proposed system. The proposed model does not aim to maximize the probability to detect fraud but rather to optimize resource allocation and improve the total efficiency and recovery of the anti-fraud system. This new approach is better suited to the concrete organization of government services because human resources are limited and tax verification is time-consuming. This highlights the value of proposing a holistic process to enhance e-Government in Tax Administrations. The current proposal establishes guidelines for a long-term research cycle that will implement and compare different algorithms and configurations. The results obtained will provide details on the comparative performance of the selected implementations.
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Notes
- 1.
An audit is usually performed in the last three years.
- 2.
\({\alpha }_{i},{\beta }_{i,} , {\gamma }_{i} , {\delta }_{i}\) considered equal to 1.
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The author would like to express his gratitude to Bernard Gaie for his valuable suggestions and careful review of the manuscript.
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Gaie, C. (2023). Struggling Against Tax Fraud, a Holistic Approach Using Artificial Intelligence. In: Gaie, C., Mehta, M. (eds) Recent Advances in Data and Algorithms for e-Government. Artificial Intelligence-Enhanced Software and Systems Engineering, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-031-22408-9_4
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