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Detecting tax evasion using artificial intelligence


Added by:   OECD analyst
Added on:   30 Sep 2026
Updated by:   OECD analyst
Updated on:   30 Sep 2026

The initiative leverages artificial intelligence and big data analytics to strengthen risk based approaches to personal income tax compliance. By identifying atypical taxpayer behaviour, it supports more targeted audits and efficient allocation of enforcement resources. The project contributes to the broader modernisation of the Serbian Tax Administration and promotes evidence based policymaking in tax administration.

Name in original language

Otkrivanje poreskih utaja upotrebom veštačke inteligencije

Initiative overview

The initiative focuses on the application of artificial intelligence and big data analytics to improve the detection of tax evasion and strengthen risk-based personal income tax compliance in Serbia. It addresses the challenge faced by tax administrations of limited human resources and growing volumes of complex data by shifting from predominantly random inspections to targeted, evidence-based audit selection.

The project responds to the need for more efficient use of enforcement capacities by analysing large datasets available to the Serbian Tax Administration and identifying atypical taxpayer behaviour that may indicate increased risk of non-compliance. Through the development of algorithms and analytical models, the initiative supports the early detection of deviations from expected patterns, enabling inspectors to focus on cases with the highest risk and potential fiscal impact.

The primary objectives of the initiative are to enhance the effectiveness of tax audits, increase voluntary compliance, and improve revenue collection outcomes while reducing the administrative burden on both the tax authority and compliant taxpayers. By strengthening analytical capabilities, the project aims to support more consistent, transparent, and data-driven decision-making in tax administration, contributing to greater public trust and integrity in the tax system.

The initiative has already demonstrated strong and tangible results, substantially improving audit targeting and efficiency by generating higher newly identified revenues with fewer field inspections. In the longer term, the initiative supports the broader modernisation of the Serbian Tax Administration and aligns with strategic objectives to adopt innovative technologies in public financial management. It also provides a scalable and replicable approach that can be extended to other tax types and compliance areas, reinforcing evidence-based policymaking and the sustainable use of artificial intelligence in the public sector.

Other relevant details

Results, outcomes and impacts: The initiative has led to more effective, risk-based tax audits and improved allocation of inspection resources. By shifting from predominantly random controls to targeted audits supported by AI-driven risk analysis, the Tax Administration achieved higher newly identified revenues with fewer field inspections. Results are measured by comparing audit volumes, detection rates, and assessed revenues before and after implementation. The initiative is expected to further improve compliance, increase voluntary tax reporting, and strengthen evidence-based policymaking as analytical models and data sources continue to expand. Challenges and lessons learned: Throughout implementation, particular attention was given to data quality, transparency of analytical models, and the preservation of human oversight, ensuring that AI-based insights complement professional judgement rather than replace it. A key lesson learned is that the sustainable use of AI in tax compliance relies on gradual implementation, continuous monitoring of model performance, and close collaboration between technical experts and tax officials. Clear accountability, alignment with existing legal frameworks, and proportional use of advanced analytics have proven essential for building institutional trust. These elements have supported the successful integration of AI-driven risk analysis into regular operations and provide a solid foundation for future scaling and replication.

About the policy initiative


Category:

  • AI policy initiatives, programmes and projects

Initiative type:

  • AI use cases/projects in the public sector

Status:

  • Active

Start Year:

  • 2021

Target Sectors:


OECD AI Principles:

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