The Role of AI in Detecting Tax Fraud and Evasion

The Growing Importance of AI in Tax Administration

Tax fraud and evasion contribute significantly to the tax gap, representing the difference between the taxes owed and the amount collected. Estimates suggest that tax fraud accounts for between 4% and 15% of the tax gap in OECD countries. In France alone, VAT fraud is estimated at €20–25 billion. Addressing this issue effectively requires innovative approaches, and AI has emerged as a powerful tool. AI’s ability to analyze vast datasets and detect patterns that elude human auditors has made it a critical asset in modern tax administration.

AI Technologies in Tax Fraud Detection

AI’s application in tax fraud detection encompasses several advanced technologies, including predictive modelling, neural networks, and natural language processing. Each of these technologies plays a unique role in identifying potential fraud and evasion.

1. Predictive Modeling: AI-driven predictive models analyze historical data to identify patterns and anomalies that may indicate fraudulent activity. For instance, these models can flag unusual transactions or discrepancies in tax filings, helping tax authorities focus their investigations on high-risk areas. Predictive modelling has effectively detected irregularities related to special grants, seasonal tax deductions, and international tax avoidance.
2. Neural Networks: Neural networks, a subset of AI, mimic the human brain’s neural connections to recognize complex patterns in data. These networks can be trained to detect subtle signs of fraud by analyzing various features of tax returns, such as income, expenditures, and asset declarations. For example, France has developed neural network-based algorithms that achieve an AUPRC score of up to 0.851, indicating high sensitivity and precision in fraud detection.
3. Natural Language Processing (NLP): NLP enables AI systems to understand and process human language, making it possible to analyze tax regulations, case law, and taxpayer communications. This technology supports the creation of AI-driven chatbots that provide taxpayers with accurate information and guidance on tax matters, improving the overall taxpayer experience.

Case Studies of AI in Action

Several countries have successfully implemented AI technologies to combat tax fraud and evasion. These case studies illustrate the diverse applications and benefits of AI in tax administration.

1. Italy: Italy’s use of AI for tax fraud detection is a prominent example of leveraging technology to enhance tax compliance. The country’s VeRa algorithm cross-references financial data from tax filings, earnings, property records, and electronic payments to identify discrepancies. This approach has led to the identification of over 1 million high-risk cases. The success of VeRa underscores AI’s potential to improve tax compliance and revenue collection.
2. Australia: The Australian Taxation Office (ATO) has employed AI models, including deep learning and gradient-boosting algorithms, to detect GST fraud and identify unpaid tax bills. By analyzing patterns of fraudulent behavior, the ATO has managed to uncover $530 million in unpaid taxes and prevent $2.5 billion in fraudulent claims. Australia’s approach demonstrates how AI can significantly enhance the effectiveness of tax fraud detection efforts.
3. Sweden: Sweden has utilized AI to streamline tax registration by identifying potential tax avoidance issues. Since 2021, AI has been used to flag risky tax avoidance behaviors during business registration, accelerating the review process and reducing manual intervention. This application highlights AI’s role in improving operational efficiencies and reducing the risk of tax evasion.
4. Vietnam: Vietnam’s adoption of AI to flag firms with unusual invoice activities represents a proactive approach to tax fraud detection. By identifying firms that issue invoices too frequently or for unusually high amounts, AI helps the government detect attempts to underreport taxable revenue (AI-driven tax advisors, 2023). This initiative demonstrates how AI can be applied to specific aspects of tax administration to address emerging fraud risks.

Challenges Involved

While AI offers significant advantages in detecting tax fraud and evasion, it also presents challenges and ethical considerations that must be addressed.

1. Privacy Concerns: The use of AI in tax administration raises privacy issues, particularly regarding the handling of personal and financial data. For instance, Italy’s Data Protection Authority temporarily banned ChatGPT due to privacy concerns and data protection violations. Ensuring that AI systems comply with privacy regulations and safeguard personal data is essential to maintaining public trust and avoiding regulatory pitfalls.
2. Data Accuracy and Quality: AI systems rely on accurate and well-structured data to function effectively. Inaccurate or inconsistent data can hinder the performance of AI algorithms and lead to erroneous fraud detection outcomes. For example, the delayed adoption of robotics in tax processing can be attributed to issues with data accuracy and standardization. Tax authorities must invest in data management practices to ensure the reliability of AI-driven fraud detection.
3. Ethical Implications: The ethical implications of AI in tax administration include the potential for biased decision-making and the risk of automated systems making erroneous judgments. The Dutch tax scandal, where AI algorithms falsely flagged family credit claims as fraudulent, highlights the risks associated with relying solely on AI for critical decisions. It is crucial to balance AI’s capabilities with human oversight to mitigate these risks and ensure fair outcomes.

The Future of AI in Tax Administration

As AI technology continues to evolve, its role in tax administration is expected to expand further. Emerging trends include the integration of AI with other technologies, such as blockchain, to enhance transparency and security in tax processes. Additionally, advancements in AI algorithms and data analytics will likely improve the accuracy and efficiency of fraud detection systems.

The future of AI in tax administration will also involve addressing the ethical and regulatory challenges associated with its use. Governments and tax authorities must develop frameworks and guidelines to ensure AI technologies are deployed responsibly and transparently. Collaborative efforts between technology providers, regulators, and tax authorities will be essential in shaping the future landscape of AI-driven tax administration.

Conclusion

AI has emerged as a transformative force in detecting tax fraud and evasion, offering significant benefits in accuracy, efficiency, and taxpayer experience. Case studies from around the world illustrate the diverse applications and successes of AI in tax administration. However, AI adoption also presents challenges, including privacy concerns, data accuracy issues, and ethical considerations. As tax authorities continue integrating AI into their operations, it is crucial to address these challenges and ensure that AI technologies are used responsibly to enhance tax compliance and protect public trust. The future of AI in tax administration holds great promise, with the potential to revolutionize tax systems, and address fraud and evasion while navigating the complex landscape of ethics and regulation.

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https://www.polytechnique-insights.com/en/columns/economy/ai-a-weapon-against-tax-fraud/

https://www.vatcalc.com/artificial-intelligence/tax-authorities-adopt-ai-for-tax-fraud-and-efficiencies/