AI flags tax evasion reliably in digitally mature states but rarely translates into uniform fiscal gains; weak data, governance and skills blunt algorithmic effectiveness in developing countries, the review finds.

Main Finding

AI tools for tax compliance achieve high detection accuracy in digitally mature economies, but their real-world effectiveness depends critically on institutional context — data quality, governance capacity, and organizational readiness. In developing countries, structural asymmetries (infrastructure, human capital, and data ecosystems) limit algorithmic performance even when technical sophistication is present. The study synthesizes these contingencies into the Adaptive AI Tax Compliance Framework (AAITCF), which prescribes context-sensitive implementation strategies across three institutional maturity tiers.

Key Points

• Scope: Systematic review of 68 peer‑reviewed articles (2015–2025) on AI in tax compliance and evasion mitigation.
• Methodology: PRISMA 2020 protocol for study selection plus textometric analysis using IRAMUTEQ.
• Three research paradigms identified:
• Machine learning and predictive modeling (detection/performance metrics).
• AI, technology, and tax compliance (behavioral/operational dynamics).
• Government, financial development, and revenue administration (institutional and governance perspectives).
• The CIMO (Context–Intervention–Mechanism–Outcome) framework structures synthesis, highlighting how institutional conditions shape both design and outcomes of AI interventions.
• AAITCF: A context-sensitive roadmap differentiating intervention design and expected outcomes across three institutional maturity tiers (digitally mature; transitional; low‑capacity).
• Main empirical pattern: High technical detection accuracy is necessary but not sufficient — outcomes diverge by context due to data gaps, weak governance, low organizational readiness, and limited human capital.
• Gaps identified: causal links from AI deployment to long‑run institutional change, impacts on taxpayer trust and compliance behavior, distributional effects and equitable fiscal governance, and rigorous causal evaluation designs.
• Critique of prior reviews: overemphasis on detection metrics, behavioral accounts without governance capacity analysis, and ethics discussions lacking theoretical embedding — this review integrates those aspects through CIMO and AAITCF.

Data & Methods

• Literature selection: PRISMA 2020 protocol applied to peer‑reviewed publications (years 2015–2025); N = 68 studies included.
• Textometric analysis: IRAMUTEQ software used to map thematic clusters, publication trends, and geographic distribution.
• Analytical framing: CIMO used to extract how context interacts with AI interventions and mechanisms to produce outcomes; synthesis informed development of AAITCF.
• Outputs: thematic mapping (three research paradigms), institutional typology for implementation, and identification of methodological & empirical gaps.
• Limitations noted by authors: reliance on peer‑reviewed articles may omit gray literature and government reports; potential geographic/language biases; heterogeneity of study designs limits meta‑analytic quantification of effects.

Implications for AI Economics

• Modeling and evaluation:
  • Economic models of AI’s fiscal impact must incorporate institutional heterogeneity — treat context as a causal moderator rather than a nuisance parameter.
  • Need for rigorous causal inference (RCTs, natural experiments, difference‑in‑differences, synthetic controls) to trace AI → short‑term detection → medium/long‑term compliance and institutional change.
  • Expand outcome metrics beyond detection accuracy to include revenue realization, taxpayer behavior, trust, administrative costs, and distributional consequences.
• Policy and implementation:
  • Prioritize investments in data quality, interoperable digital infrastructure, and tax administration capacity before scaling sophisticated AI models.
  • Develop governance frameworks (transparency, auditability, accountability) and workforce training to ensure organizational readiness and mitigate ethical/ fairness risks.
  • Tailor AI strategies to institutional maturity: simpler, robust tools and capacity building in low‑capacity settings; advanced, integrated systems with governance safeguards in mature settings.
• Research agenda:
  • Comparative cross‑country studies to quantify how institutional traits mediate AI effectiveness.
  • Longitudinal research on AI’s role in institutional change and fiscal trust.
  • Interdisciplinary work linking technical performance, behavioral responses, legal/regulatory design, and political economy of revenue administration.
• Equity and welfare concerns:
  • Assess who bears costs/benefits of algorithmic enforcement (e.g., small businesses, informal sector).
  • Incorporate fairness and privacy impact assessments into economic evaluations of tax AI deployments.

If helpful, I can extract actionable checklists from the AAITCF for each institutional tier (mature, transitional, low‑capacity), or propose concrete empirical designs to identify causal effects of AI on tax revenues and taxpayer trust.