An AI-driven Approach to Improve the Adjudication Landscape in CBIC

The quasi-judicial role of Indian Revenue Service (Customs & Indirect Taxes) officers is a unique feature of India’s fiscal administration, as laws such as the Central Excise Act, 1944, Customs Act,1962, Service Tax (Finance Act, 1994) and the Central Goods and Services Tax Act, 2017 vest them with statutory authority to issue show-cause notices, conduct adjudicatory proceedings including personal hearings, evaluate documentary and electronic evidence, and pass reasoned orders involving classification, valuation, exemption eligibility, and tax or duty demands, penalties, and confiscation.
Unlike generalist administrative services where quasi-judicial functions are intermittent and closely embedded within executive governance, Customs and GST officers perform adjudication as a specialized and continuous function requiring deep technical knowledge of tariff law, international trade, and indirect tax jurisprudence. Their decisions are subject to a structured appellate hierarchy—culminating in the Customs, Excise and Service Tax Appellate Tribunal (CESTAT) and the GST Appellate Tribunal (GSTAT), as applicable, and constitutional courts. This quasi-judicial role has, over the years, come under increasing scrutiny as a result of the Department’s adjudication success rate in higher forums. This paper seeks to analyse in detail the reasons for the poor success rate of the Department’s adjudication orders in higher forums and prescribe suitable solutions to improve the same. In particular, considering the widespread adoption of Artificial Intelligence (AI) in the legal space, it is believed that the use of AI in adjudication can radically alter the Department’s success rate in judicial forums, thereby leading to a cascading of positive outcomes for officers.

GST, Customs, Central Excise, Service Tax, Adjudication, CESTAT, GSTAT, Show Cause Notice, Artificial Intelligence, Appeal, Evidence

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