AI tools cut costs and boost efficiency in global tax fraud detection systems

Artificial intelligence is fast becoming a vital tool in the fight against tax fraud, reshaping how governments worldwide track illicit financial activity. A new study highlights how advanced technologies are enhancing accuracy, efficiency, and governance in tax systems across both advanced and emerging economies.

The research, titled “Tax Fraud Detection Using Artificial Intelligence-Based Technologies: Trends and Implications” and published in the Journal of Risk and Financial Management, provides one of the most comprehensive examinations to date of how machine learning, blockchain, and other AI-driven tools are revolutionizing tax oversight. Covering 163 peer-reviewed studies published between 2014 and 2024, it introduces a structured Adaptive AI Tax Oversight (AATO) framework designed to modernize detection and enforcement.

How artificial intelligence is transforming tax oversight

Tax fraud is a persistent global challenge, with annual losses surpassing USD 1 trillion in corporate tax revenue alone. The authors argue that traditional enforcement methods are no longer sufficient, particularly as fraud schemes become more complex and cross-border in nature. AI is presented as a turning point, enabling governments to move beyond manual detection into systems that can process vast datasets, predict fraudulent activity, and adapt in real time.

The AATO framework outlined in the paper illustrates this shift through six stages: data aggregation, anomaly detection, predictive analysis, adaptive learning, decision support, and continuous evolution. By integrating supervised and unsupervised learning with blockchain-enabled transparency, the framework offers a dynamic model for tax administrations to improve fraud detection accuracy while reducing compliance costs.

The review finds that AI tools deliver measurable improvements in efficiency. Machine learning algorithms can identify subtle anomalies in taxpayer data that would otherwise go undetected, while neural networks achieve accuracy levels exceeding 90 percent in case studies from Rwanda and other contexts. Blockchain technology further strengthens transparency and traceability, helping tax authorities validate transactions with reduced reliance on intermediaries. Together, these technologies form the backbone of a new generation of oversight aligned with the OECD’s Tax Administration 3.0 vision.

What global evidence reveals about effectiveness and challenges

The paper integrates results from some of the most cited research in the field. A 2016 study by Calafato and colleagues demonstrated how controlled natural language simplified fraud detection rules for experts, while Alm and co-authors in 2019 showed how emerging digital tools improved compliance enforcement. More recent work by Savic and collaborators in 2022 applied hybrid unsupervised methods that reached 98 percent accuracy in detecting tax evasion outliers. Neural network applications by Murorunkwere and colleagues in 2022 yielded fraud detection accuracy rates above 90 percent, confirming the potential of AI for income tax monitoring.

Case studies reveal diverse applications across regions. In Armenia, machine learning enhanced audit efficiency, particularly for new companies with limited historical data. In Lithuania, data mining helped reduce revenue losses by improving tax evasion detection. In Saudi Arabia, hybrid supervised–unsupervised frameworks improved fraud identification under the national tax authority. Open frameworks developed in Europe and Africa further showcased AI’s ability to optimize fraud detection while reducing administrative burden.

Despite the promising evidence, challenges remain widespread. Data privacy concerns are a major barrier, particularly in countries where regulatory safeguards are weak. Integration with legacy IT systems often proves costly and technically complex, while the shortage of skilled professionals limits effective deployment. The review also highlights risks of algorithmic bias and ethical dilemmas around taxpayer surveillance.

Financial constraints add another layer of difficulty. Technologies like blockchain require significant upfront and maintenance investments, while deep learning models demand extensive computational resources. For lower-income economies, these hurdles make incremental adoption essential, starting with low-cost analytics before transitioning to advanced frameworks.

What the future holds for governments, auditors, and taxpayers

AI’s role in tax fraud detection is not purely technical but also institutional and social. For advanced economies, the priority lies in refining interoperability, addressing algorithmic bias, and ensuring taxpayer trust. These administrations are urged to align AI integration with broader digital governance strategies to maintain transparency and accountability.

On the other hand, low- and middle-income countries are advised to focus on building digital infrastructure, securing reliable data, and adopting AI gradually. Policymakers in these regions should prioritize capacity building, including training tax officials and forming partnerships with technology providers and universities. Incremental integration of supervised learning and data analytics is presented as the most practical pathway before scaling up to blockchain or deep learning.

Audit professionals are also set to experience a transformation. AI strengthens audit quality by detecting irregularities more efficiently and allowing auditors to concentrate on advisory and analytical tasks rather than repetitive manual checks. However, professionals will need to update their technical, statistical, and legal expertise to keep pace with evolving systems. Continuous training and strict ethical standards are identified as critical to ensure independence and avoid over-reliance on automated decision-making.

For taxpayers, AI-based systems can enhance fairness and consistency, reducing errors and ensuring equal treatment of similar cases. Yet risks remain. Automated classifications may misidentify compliant taxpayers as fraudulent, raising concerns over due process and accountability. The study recommends that tax administrations establish clear communication strategies, accessible appeals processes, and robust legal safeguards to protect taxpayer rights while preserving the efficiency benefits of automation.

Future research, as the authors envision, should go beyond technical performance to examine how AI adoption influences trust, legitimacy, and equity in tax systems. Comparative studies between developed and developing economies are highlighted as a priority, as are investigations into the ethical and governance implications of algorithm-driven tax oversight.