[Syed Alwaz Asif is a penultimate year law student at Dr Ram Manohar Lohiya National Law University, Lucknow.]
In an era where organizations and governments are inundated with vast amounts of data, the challenge of effectively detecting and preventing tax fraud has intensified. The overwhelming magnitude of information available often surpasses human capacity to discern intricate patterns and irregularities. However, artificial intelligence (AI) provides a potential solution to tax fraud detection challenges due to its ability to efficiently process and analyze vast datasets.
One of the most compelling advantages of employing AI in tax fraud detection is its capacity for real-time monitoring. The tax authorities could harness AI/ML-driven audits to keep an eye on conspicuous transactions involving large sums of money such as property purchases, international fund transfers, and raise red flags, especially when such transactions are inconsistent with the taxpayer’s declared income or previous financial behaviors. Traditional methods of detecting tax fraud have predominantly been reactive, identifying fraudulent activities only after they have transpired. In contrast, AI facilitates the continuous surveillance of transactions and behaviors, enabling the immediate identification of suspicious activities. This proactive approach not only aids in the timely detection of potential fraud but also acts as a deterrent, preventing fraudulent activities from materializing.
This article aims to delve into the intricacies of leveraging machine learning, a subset of AI, in the detection of tax fraud. It seeks to explore the transformative potential of AI-driven models in enhancing tax fraud detection mechanisms. Furthermore, the article will address the challenges and considerations that emerge as we tread the path towards the comprehensive integration of AI into income tax systems.
Harnessing Machine Learning to Combat Tax Evasion and Strengthen Fiscal Foundations
Tax fraud, commonly referred to as tax evasion, manifests when individuals or corporate entities deliberately circumvent their fiscal obligations by underreporting income, asserting unfounded deductions, or clandestinely safeguarding assets. The ramifications of tax fraud extend beyond mere fiscal implications; it poses a grave threat to the very fabric of a nation’s economic structure. Notably, as of June 2023, India’s tax revenue, when measured as a percentage of its gross domestic product (GDP), stood at a mere 6.1%. This marked a discernible decline from the preceding figure of 7.5% recorded in March 2023. A diminished tax-to-GDP ratio invariably precipitates adverse economic consequences, including reduced investment capacities, exacerbated fiscal deficits, and an escalation in national debt. Each of these factors, either directly or indirectly, wields the potential to influence a nation’s economic growth trajectory and inflationary trends. Consequently, it becomes imperative to explore innovative methodologies, such as AI/ML and advance data analytics to advance tax enforcement and to effectively combat tax evasion and restore public confidence in the sanctity of the tax regime.
The Income Tax Department’s Pioneering step to Embrace AI/ML for Advance Tax Enforcement
The evolution of tax enforcement has witnessed a significant shift with the integration of advanced technologies, particularly AI and ML. The Government’s introduction of Project Insight is a testament to this transformation. This initiative, spearheaded by the Income Tax Department, leverages AI/ML and big data analytics to meticulously monitor high-value transactions, thereby aiming to unearth tax evaders and curb the circulation of black money. The project’s tri-fold objectives are clear: promote voluntary compliance, instill confidence in the tax system’s fairness, and ensure judicious tax administration.
The phased rollout of an integrated data warehousing and business intelligence platform under Project Insight has given birth to two pivotal centers: the Income Tax Transaction Analysis Centre (INTRAC) and the Compliance Management Centralized Processing Centre (CMCPC). INTRAC, in particular, stands out for its utilization of data analytics in tax administration, encompassing a wide array of tasks from data integration and processing to alert generation and research support.
The Income Tax Department’s strategic deployment of AI/ML is not limited to data warehousing. By harnessing data mining and big data analytics, the department aims to identify tax evaders on social media platforms, thereby pinpointing discrepancies between spending patterns and declared income. The advantages of integrating AI into tax fraud detection are manifold. Not only can AI process vast datasets at a speed incomparable to human auditors, but it can also identify intricate fraud patterns, such as recurring false deductions or unusual tax credit claims. This precision, combined with real-time monitoring capabilities, positions AI as a formidable tool against tax fraud.
Furthermore, AI’s ability to simultaneously scan and analyze multiple tax-related datasets, such as financial statements and property records, amplifies its potency in detecting cross-border fraud. This is particularly crucial in unmasking tax evaders concealed behind intricate networks of front companies and shell firms.
Bridging the capabilities of AI with its specialized branch, it’s essential to delve deeper into the nuances of how these technologies function in the realm of tax enforcement. While AI provides a broad spectrum of capabilities, including the scanning and analysis of vast tax-related datasets, it is the specialized techniques within machine learning that fine-tune this process. Machine learning, a subset of AI, offers two particularly relevant tools for tax enforcement: supervised and unsupervised learning. While supervised learning focuses on known outcomes, unsupervised learning uncovers patterns across datasets that might otherwise remain undetected. However, the application of these tools is not devoid of human intervention. Data scientists, in collaboration with subject matter experts, play a pivotal role in fine-tuning algorithms, interpreting findings, and determining subsequent actions.
In the realm of supervised learning, one of its primary applications in tax enforcement is predicting the likelihood of noncompliance. By labeling tax returns found to be noncompliant, the system learns patterns and can predict future noncompliance based on these patterns. On the other hand, unsupervised learning, through methods like clustering and anomaly detection, facilitates the discovery of undetected patterns and potential noncompliance indicators.
While machine learning offers a more refined approach to selecting returns for compliance activities, it is not without challenges. The very strengths of machine learning, its ability to absorb vast amounts of data and identify subtle patterns, also introduce complexities. The “black box” nature of certain algorithms can make it difficult to understand and explain the rationale behind specific predictions or decisions. This opacity can lead to legal and ethical challenges, especially when decisions lack transparency or are perceived as biased. As we delve deeper into the integration of AI in tax systems, it becomes imperative to address these challenges, ensuring that the pursuit of technological advancement does not compromise fairness and justice in tax administration.
AI Black Box: A new era of Legal Challenges
The integration of AI/ML in tax enforcement, while promising enhanced scrutiny and accuracy in tax filings, simultaneously ushers in a new era of legal challenges. These challenges will begin to manifest in the form of litigations at various judicial levels, from Income Tax Tribunals to the apex Supreme Court. Interestingly, the crux of these litigations would shift in future whereby, instead of solely grappling with the intricacies of the tax code, they now intertwine with the complexities inherent to AI technology.
Central to these complexities is the ‘Black Box Effect’ in data analysis. This phenomenon pertains to AI systems that, while producing actionable insights, remain reticent about their internal mechanics. In essence, while the outputs are visible and often beneficial, the underlying processes leading to these outputs remain shrouded in mystery, akin to a sealed ‘black box’. This inherent opacity in AI/ML-driven algorithms is what gives rise to the term ‘black box complexity’.
At its core, the Black Box Problem encapsulates the challenges posed by our limited understanding of AI’s decision-making pathways and our consequent inability to anticipate its decisions or outcomes. The foundation of these AI systems, particularly deep neural networks, is built upon the concept of the artificial neuron. Although inspired by the biological neurons present in human and animal brains, the artificial neuron is not a digital replica of its biological counterpart. Instead, it is designed with the ambition to replicate the learning capabilities inherent to biological neurons. Within these networks, no singular neuron is responsible for a specific aspect of the decision-making process.[i]Instead, they operate collectively, with thousands or even hundreds of thousands of neurons collaborating to reach a conclusion. The complexity arises when these machine-learning algorithms, by considering multiple variables simultaneously, discern patterns that are beyond human comprehension or visualization. As tax enforcement increasingly relies on such AI-driven decisions, the legal fraternity is tasked with navigating the uncharted waters of justifying and challenging decisions made by an inscrutable system.
AI-driven Tax Audit System and its Implications
The Income Tax department’s ambitious endeavour to harness vast data reservoirs, particularly from social media to pinpoint tax discrepancies presents a multifaceted conundrum. While the intent is to identify and rectify tax disparities, the methodology employed, especially the reliance on unsupervised learning by AI, raises profound legal and ethical concerns.
Consider this illustrative scenario: the tax authority commissions a state-of-the-art deep neural network AI system to meticulously audit tax returns, aiming to unearth potential tax evasion. Empowered with access to a plethora of data sources, from taxpayers’ social media footprints to their real-time financial transactions and historical tax data, this AI rapidly evolves into a formidable tool, adept at spotlighting suspicious tax activities. It begins to meticulously monitor social media, especially posts flaunting opulent lifestyles, juxtaposing them against tax return data to discern incongruities. Concurrently, it scrutinizes real-time financial transactions, hunting for telltale signs of concealed income or undisclosed assets. The AI’s omnipresence is further accentuated as it dispatches automated inquiries to taxpayers and subtly showcases its surveillance prowess through strategic social media posts.
However, beneath this veneer of efficiency lies a labyrinth of complexities. The intricate web woven by the deep neural networks, comprising thousands of interconnected artificial neurons, renders the AI’s decision-making process esoteric.[ii] The tax authority grapples with a pressing dilemma: comprehending and justifying the AI’s decisions. Why does the AI flag certain taxpayers and overlook others? This opacity paves the way for a slew of legal challenges, especially from aggrieved taxpayers who perceive themselves as unjustly singled out. They may contest the AI’s determinations in judicial forums, alleging unwarranted targeting or privacy infringements. The specter of the AI misinterpreting a jesting social media post or misconstruing context, resulting in erroneous flagging, further exacerbates these concerns.
The ramifications extend beyond individual grievances. A pervasive sense of surveillance might deter businesses and individuals from transparent financial disclosures online, apprehensive of the AI’s scrutiny. This could catalyze a surge in clandestine financial activities, such as resorting to cryptocurrencies to obfuscate transactions from the AI’s watchful gaze. Consequently, the tax authority’s AI initiative might ignite fervent debates, necessitating lucid regulations and ethical guidelines governing AI applications and data privacy. The discourse would inevitably revolve around striking a judicious balance between leveraging AI for optimal tax revenue collection and safeguarding sacrosanct individual rights and privacy. The trajectory of this AI initiative, be it triumphant or otherwise, will indubitably influence the AI adoption strategies of other governmental departments.
Building on the complexities introduced by the integration of AI in tax enforcement, the European Union (EU) has recognized the imperative need for transparency and accountability in algorithmic decision-making. A study conducted by the EU Parliament’s Science and Technology Options Assessment (STOA) in 2019 delineated a comprehensive framework to address the multifaceted challenges posed by algorithmic processes. This framework, while broad in its scope, has significant implications for tax authorities leveraging AI, especially in light of the concerns highlighted previously.
The STOA study proposes four pivotal policy options:
- Awareness Raising: This emphasizes the importance of education in understanding algorithmic processes, bolstering the role of watchdogs in monitoring AI applications, and empowering whistleblowers to report potential misuses.
- Public-Sector Accountability: Given the public trust vested in tax authorities, this policy underscores the need for transparency and responsibility when deploying AI tools for decision-making.
- Regulatory Oversight and Legal Liability: This option advocates for a robust legal framework that holds entities accountable for AI-driven decisions, ensuring that taxpayers’ rights are safeguarded.
- Global Coordination: Recognizing the borderless nature of technology, this policy emphasizes international collaboration in establishing standards for algorithmic governance.
The proposal of algorithmic impact assessments mirrors the environmental impact assessments, aiming to evaluate the potential ramifications of AI applications before their deployment. The IEEE P7001 standard, dedicated to enhancing the transparency of autonomous systems, establishes a gold standard for ensuring comprehensibility in AI functionalities.
In the context of tax enforcement, these EU guidelines offer a roadmap for balancing the efficiency gains from AI with the ethical and legal challenges it presents. By adopting such measures, tax authorities can ensure that while they harness the power of AI to enhance revenue collection, they do so without compromising the rights and privacy of taxpayers.
As we stand on the precipice of a new era in tax enforcement, the integration of AI and ML offers unparalleled opportunities to revolutionize the way tax authorities operate. The potential of AI to process vast amounts of data, identify intricate patterns, and provide real-time monitoring is undeniable. However, with great power comes great responsibility. The complexities introduced by the ‘Black Box Effect’ and the challenges of ensuring transparency and fairness in AI-driven decisions necessitate a thoughtful and measured approach.
Conclusion
The fusion of AI and tax enforcement is not merely a technological advancement; it is a paradigm shift in how governments interact with their citizens. While the potential benefits are immense, from increased revenue collection to the timely detection of tax evasion, the ethical and legal implications cannot be overlooked. The European Union’s guidelines regarding transparency in algorithmic design provide a beacon, illuminating the path forward. By embracing transparency, prioritizing education, public system accountability and fostering global collaboration, we can harness the full potential of AI in tax enforcement while upholding the principles of justice, fairness, and respect for individual rights. As we embark on this journey, it is imperative to remember that technology should serve humanity, not the other way around. The future of tax enforcement lies not just in algorithms and datasets but in the judicious and ethical application of these tools for the greater good.
– Syed Alwaz Asif
[i] Flach, Peter, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, United Kingdom, Cambridge University Press, 2012.
[ii] Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning (Adaptive Computation and Machine Learning series), MIT Press (2016).