A CRITICAL STUDY OF THE ROLE OF ARTIFICIAL INTELLIGENCE IN STRENGTHENING INSOLVENCY RESOLUTION PROCESSES UNDER THE IBC FRAMEWORK

The Insolvency and Bankruptcy Code, 2016 (IBC) represents India's most significant structural reform to corporate debt resolution, establishing a time-bound, creditor-driven framework that displaced the fragmented pre-existing regime. Despite this legislative achievement, persistent institutional bottlenecks including prolonged resolution timelines, case backlogs at the National Company Law Tribunal (NCLT), information asymmetry between creditors and corporate debtors, and systemic undervaluation of distressed assets continue to undermine the IBC's transformative objectives.1 This paper critically examines how Artificial Intelligence (AI) can address these structural deficiencies to strengthen insolvency resolution processes under the IBC framework.
The paper analyses AI's operational application across three critical dimensions of the CIRP: predictive analytics for early financial distress identification, automated document processing and claims verification, and AI-assisted asset valuation. Drawing on comparative experiences from the United States, the United Kingdom, and the European Union, the paper identifies both the transformative potential and the regulatory risks including algorithmic opacity, bias in credit assessment, and data security vulnerabilities that AI deployment in insolvency proceedings entails.2
The paper argues that AI integration within IBC proceedings is not merely a technological upgrade but a doctrinal imperative: without systemic reform supported by intelligent automation, the IBC's mandate of maximising asset value and ensuring timely resolution will remain aspirational rather than operational. The paper concludes with a multi-tiered reform proposal anchored in regulatory accountability, institutional capacity-building, and an AI Governance Framework tailored to India's insolvency architecture.

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