Artificial intelligence (AI) is revolutionizing tax fraud detection globally, enhancing governments’ capabilities to track illicit financial activities. A comprehensive study published in the Journal of Risk and Financial Management examines how machine learning and blockchain optimize tax oversight. It introduces the Adaptive AI Tax Oversight (AATO) framework, which includes six critical phases: data aggregation, anomaly detection, predictive analysis, adaptive learning, decision support, and continuous evolution.
The findings show AI significantly improves efficiency in identifying tax fraud, with studies reporting accuracy rates above 90%. Yet, challenges persist, including privacy concerns, integration with existing systems, and the need for skilled professionals. Advanced economies are encouraged to address algorithmic bias and transparency, while lower-income nations focus on digital infrastructure and incremental AI adoption.
For audit professionals, AI enhances quality but necessitates updated skills and ethical standards. The study concludes that ongoing research should explore AI’s broader societal impacts on trust and equity in tax systems.
