Comparative Study of Global Best Practices: Role of AI in Pre-Pack Insolvency in the UK, US, and India

Abstract

The pre-pack insolvency process has become one of the major tools in global restructuring as it contributes to maintaining operational continuity, minimising value destruction through long and drawn-out administrations, while offering some element of expedite resolution for distressed companies. In the UK and US, pre-pack insolvency has been shaped by decade’s worth of legislation changes, court cases and professional practice after maturity. In this context, the introduction of AI in insolvency proceedings has been revolutionary. The utility of AI goes beyond dealing with just another tool for processing technology and becomes a larger structural capacity that can help to understand financial due diligence, demonstrate early-stage distress indicators, automate large scale data reviews, provide increased transparency around process flow and assist practitioners (including courts) in arriving at complex decisions more accurately.

In the UK, AI tools complement regulatory reforms and practitioner-led systems, while in the US, they are integrated into court-centric insolvency procedures, especially in Chapter 11 reorganizations. India, in contrast, is at a nascent stage in both its pre-pack framework, introduced only in 2021 for MSMEs, and its AI adoption in insolvency law. This paper undertakes a comparative study of how AI has been used or could be further leveraged in the UK and US, and identifies lessons that may be drawn for India. The analysis reveals that while the UK emphasizes practitioner accountability and the US emphasizes judicial oversight supported by AI, India must prioritize infrastructural development, regulatory clarity, and professional training to meaningfully adopt AI in its pre-pack framework. By synthesizing global best practices, the paper recommends policy interventions for India to integrate AI into its insolvency system in order to enhance speed, credibility, and fairness in resolution.