Smart Defaults: An AI – Pre-Insolvency Early Warning System Integrated with India’s Information Utilities

This paper shall advocate for solving the need for a predictive and data driven approach to insolvency in India. While the IBC, 2016 has transformed the landscape, it is noticed that the legislation only arrives into the picture after a significant default has already occurred. As per the data available by December of 2024, more than 8000 CIRPs have been admitted, but with an average lowly rate of creditor recovery at 32.8 percent and nearly 44 percent of closed processes ending with liquidation. In comparison, jurisdictions like EU and UK have begun their integration of AI-powered Early Warning Systems (EWS), enabling timely intervention and reduction in value erosion.

This paper shall intend to propose a roadmap for design and implementation of an AI-powered Early Warning System integrating India’s digital infrastructure including Information Utilities. The proposed system would combine data from GST filings, MCA records, NeSL agreements, Credit Scores with the use of supervised learning models to generate risk scores and explainable dashboards. The paper shall also conduct a critical examination of technical and legal challenges such as data silos, privacy and consent under the DPDP Act, 2023 and regulatory harmonization among other related statutes.

By conducting a comparative analysis with the EU’s Directive 2019/1023, the UK’s Red Flag Alert, and Singapore’s MAS-backed AI systems, the paper shall adjudicate on the feasibility and impact of such a system, while offering a phased implementation strategy beginning with Large Corporate NPA-holders and recommend legal interventions for recognition of AI-EWS outputs as advisory triggers for pre-insolvency mediation or restructuring. It will also comment on algorithmic fairness, stakeholder consultation and effective redressal mechanisms.

By moving towards a predictive insolvency regime from a reactive one, Indian insolvency landscape can prevent value erosion, improve recovery and enhance stability in the framework. The research will provide actionable insights for policy makers, regulators, practitioners, contributing to technological intervention in the current traditional insolvency regime.