Introduction
A. Background and context
The enactment of the Insolvency and Bankruptcy Code, 2016 by Parliament marked a complete shift in the approach of the Government of India towards corporate distress.1 The winding up of companies had long been a protracted and circuitous process, governed by a maze of company laws, tribunals and courts over several decades, with generally disappointing outcomes for creditors and debtors alike. The Code sought to streamline this fragmented landscape by establishing a resolution framework that placed a deadline on the resolution process, set at 180 days and extendable to 330 days in defined circumstances, and that placed creditors at the helm of the resolution proceedings.
In practice, the picture that has emerged is more complicated. The National Company Law Tribunal, the principal adjudicatory body under the Code, has faced litigation on an unprecedented scale that it was never designed to handle. Records indicate that the matters pending before the NCLT as of recent years numbered in the hundreds of thousands,2 and most cases have taken considerably longer to resolve than the timelines stipulated by the Code. These judicial delays have imposed economic costs and have undermined public trust and confidence in the Code as a swift and efficient insolvency instrument.
Against this backdrop of institutional stress, the potential of AI has become a subject of growing scholarly and policy interest. AI technologies are no longer a mere possibility in legal systems but an operational reality. Singapore, Estonia and the United Kingdom have already introduced AI-driven tools for case management, predictive scheduling and judicial support.3 The e-Filing system of the Supreme Court and the eCourts Mission Mode Project represent early, though significant, steps towards the digitalisation of judicial administration in India. The question that has received far less attention is whether and how AI can be directed towards enhancing insolvency resolution in particular, an area characterised by financial complexity, legal technicality and high commercial stakes.
This paper proceeds on the premise that incremental procedural changes are insufficient to secure the future of insolvency governance in India. The volume of corporate insolvencies, the complexity of multi-jurisdictional group insolvencies and the information asymmetry that pervades the insolvency process together suggest a need for structural technological intervention. At the same time, the paper remains alert to the difficulties that accompany algorithmic decision-making in the legal domain, including the explainability of legal decisions, procedural fairness and the essential role of human judgment in adjudication.
B. Research objectives
This study pursues three principal objectives. First, it seeks to identify and understand the structural flaws that affect insolvency resolution and judicial case management under the IBC as it currently stands. Second, it reviews various AI-enabled tools and methods that hold genuine promise for improving resolution outcomes, in particular predictive analytics, automated document review, ODR platforms and machine-learning-driven risk assessment. Third, it critically examines the legal and institutional obstacles to the use of AI in insolvency proceedings and proposes a governance structure that balances technological efficiency against judicial accountability and due process protections.
Literature review
A. Structural deficiencies in India’s insolvency framework
In recent years, scholarship on the Indian insolvency system has been particularly active, with much of it focused on identifying persistent inadequacies in the framework. In his analysis of the evolution of insolvency and bankruptcy law in India, Chandra Shekhar offers a considered account of the Code’s development and the measurable improvements it has produced, such as an increase in creditor recovery rates and a reduction in resolution timelines relative to the regimes that preceded it. Shekhar nonetheless records, with candour, the various issues that have prevented the framework from realising its transformative potential.4 Systemic problems such as delays at the NCLT arising from capacity constraints, unresolved questions concerning sector-specific insolvency and the absence of an effective cross-border insolvency process are identified as concerns that legislative amendments have not yet addressed.
Prakriti Raj and Priyanshu Kumar Tripathy provide a detailed analysis of one of the most significant contemporary challenges in insolvency law, namely the resolution of corporate groups. Their research demonstrates that the absence of statutory group insolvency procedures leads to the separate resolution of economically connected entities, causing procedural fragmentation, value loss and coordination failures. Drawing on the UNCITRAL recommendations and a comparative study of jurisdictions, they argue strongly for a harmonised group insolvency regime and note its suitability for AI-based coordination tools.5
B. AI and dispute resolution: emerging scholarship
The volume of academic literature on AI in dispute resolution has grown significantly in recent years, with several works of particular relevance to this study. The empirical study by Janees Rafiq offers a real-world perspective on the introduction of AI technologies such as machine learning, natural language processing and automated case management systems into the Indian judiciary, illustrating their current standing within the country’s judicial landscape.6 While recognising the considerable potential of AI to minimise delays and enhance case throughput, Rafiq remains mindful of the ethical implications of algorithmic adjudication, including transparency, explainability and the need to preserve human oversight in AI-driven legal processes.
The study by Mathusha Francis and co-authors is a systematic literature review of industry-specific applications of AI in proactive dispute management in the construction industry, yet its findings have wider application. Their results confirm the effectiveness of machine-learning algorithms and predictive analytics tools in detecting early signs of disputes and taking proactive measures to prevent them from escalating into formal proceedings.7 This insight is valuable in insolvency proceedings, where the early recognition of financial difficulty can increase the prospects of a successful resolution.
Nimisha Tambekar examines the possibilities, as well as the institutional parameters, for the responsible adoption of AI in the Indian legal profession. Her research reveals that AI-based legal research and analysis tools, document automation and contract analysis are already enhancing the productivity of law firms and legal departments. Of equal importance, she identifies professional training and ethical safeguards as preconditions for the sustainable integration of AI,8 a point that applies equally to the deployment of AI in insolvency courts and resolution proceedings.
C. AI adjudication and the limits of algorithmic decision-making
The work of Khokhar, Anees and Gujjar maintains a sustained and critical focus on the normative boundaries of AI in adjudication. In their comparative analysis of the use of AI in corporate adjudication systems across several jurisdictions, they conclude that AI technologies can render adjudication more efficient and reduce the time and expense involved, but cannot fully replace human judges. The authors discuss the present difficulties with contextual reasoning, ethical judgment and interpretive flexibility as longstanding challenges in algorithmic adjudication. They advocate a hybrid adjudicatory model in which AI assists judges who nonetheless retain a central role.9 This paper adopts that line of reasoning as the normative basis for its proposed adjudicatory models.
The study by Vikas Verma and Ashok Yadav on the use of AI in consumer redressal systems is relevant to the concern that effective and adequate regulatory frameworks must be developed for AI-saturated environments. Their proposal for a hybrid approach to online dispute resolution, digital mediation and algorithmic accountability mechanisms is especially pertinent to the insolvency regime, in which various stakeholders, including creditors, resolution professionals, operational creditors, employees and regulators, must interact within a tightly structured procedural framework. The authors’ emphasis on fairness, accessibility and transparency as essential parameters10 is highly relevant to the governance challenges arising from the use of AI in insolvency proceedings.
D. Online dispute resolution and insolvency
The paper by Jyotirmoy Banerjee and Bhrigu Raj Maurya on the role of ODR in insolvency law is a significant contribution to the literature relevant to this study. The authors illustrate how the insolvency frameworks of India and Vietnam can inform the implementation of online dispute resolution processes and platforms in financial distress cases, thereby enhancing accessibility, efficiency and transparency. Their proposed integration of AI, blockchain technology and virtual dispute resolution systems into insolvency processes offers a legally sound and institutionally feasible blueprint for technological modernisation.11
E. Financial regulation and digital governance
In his research on a digital accounting framework for non-bank financial institutions in Egypt, Amin Elsayed Ahmed Lotfy examines the interface between digital governance and financial regulation. Lotfy’s analysis of how a digitised financial reporting system, incorporating Basel III standards and IFRS principles, can enhance the quality of information available to insolvency administrators and NCLT benches in India is instructive.12 The empirical analysis by G. Yoganandham on the impact of AI in the Indian financial landscape further reinforces the case for AI-led governance, while presenting an inventory of the cybersecurity and regulatory challenges that must be addressed13 if AI is to be widely adopted across a large financial sector.
Research methodology
This study adopts a doctrinal and analytical method, drawing principally on secondary sources. The doctrinal component involves a detailed analysis of the provisions of the Insolvency and Bankruptcy Code, 2016, the framework of the Insolvency and Bankruptcy Board of India, and the rules and judgments of the NCLT. The analytical layer engages a selected set of recent, peer-reviewed scholarship, policy reports and institutional publications in order to build a theoretically sound and normatively defensible argument.
The subject of the research is interdisciplinary, drawing on insolvency law, legal technology, financial regulation and institutional governance. By comparing international experience, including ODR practice in the European Union, experiments with AI in adjudication in Singapore and Estonia and the UNCITRAL model frameworks,14 the study situates India’s position and identifies lessons that may be transferable. It relies on secondary empirical data drawn from verified institutional sources, such as the Annual Reports of the IBBI and published statistics of the NCLT, to support its descriptive claims regarding the current state of insolvency governance.
AI-driven insolvency resolution: opportunities and applications
A. Predictive analytics and early financial distress detection
A significant and impactful use of AI in insolvency lies in its capacity to predict financial distress. The insolvency process in India is essentially reactive: by the time an application is filed before the NCLT, the debtor’s condition has generally deteriorated considerably, and the likelihood of value recovery is correspondingly diminished. Machine-learning models can detect signs of financial distress in financial data months before the formal thresholds of insolvency are reached, drawing on balance sheets, cash flow statements, credit bureau data and market signals.
If such early-warning systems were incorporated into the regulatory oversight frameworks of the IBBI and the Reserve Bank of India, they could facilitate pre-insolvency action, including restructuring negotiations, consensual debt workouts and targeted regulatory engagement, thereby maximising value creation for all stakeholders. The literature on financial sector monitoring and the value of predictive tools in solvency assessment is repeatedly reinforced in this study. Lotfy’s work on digital accounting in financial institutions illustrates15 how technology-based reporting systems can offer regulators a more comprehensive and up-to-date understanding of the financial position of institutions, a capability that extends naturally to corporate insolvency monitoring.
More specifically, the Yoganandham study on the integration of AI in the financial sector16 notes that AI-powered innovations have significantly improved operational efficiency and fraud detection in the banking sector. If applied to the detection of corporate financial distress, such capabilities could shift the course of insolvency proceedings in India from a liquidation-driven outcome towards a genuinely resolution-oriented one.
B. Automated document review and resolution planning
The IBC process is extremely document-intensive. Vast quantities of documentation are generated during claims verification, the analysis of financial information, asset valuation, the evaluation of resolution plans and communication with creditors, all of which must be reviewed, processed and analysed within statutory deadlines. This process is typically hampered by information overload, limited institutional support and the time constraints faced by the resolution professional.
Document review software based on natural language processing (NLP) and machine learning (ML) offers a viable solution. As Tambekar’s research on the use of AI tools in the legal field indicates,17 document automation and AI-powered document analysis platforms can significantly reduce the time spent on document-heavy legal processes while maintaining the same standard of analysis. In the insolvency context, AI could be used to automate the initial screening and classification of creditor claims, detect discrepancies in financial statements, flag suspicious transactions for further investigation and compare proposed resolution plans against applicable laws and case law.
A systematic literature review by Francis and colleagues found18 that AI tools capable of analysing vast quantities of unstructured data, such as contracts, correspondence and financial information, can detect patterns and risks that human review may overlook. This is especially beneficial in complex corporate insolvency situations, where multiple classes of creditors, intra-group transactions and disputes over asset ownership often require the review of an enormous volume of relevant documentation, a task that is effectively impossible using traditional methods.
C. Online dispute resolution and insolvency proceedings
One of the most immediately actionable technological innovations for the near to medium term is the integration of online dispute resolution platforms into the insolvency process. The comparative study by Banerjee and Maurya of ODR in the Indian and Vietnamese insolvency systems19 is compelling and shows how ODR platforms can enhance accessibility for geographically dispersed creditors, mitigate procedural delays and lighten the burden on formal tribunals.
Although the NCLT’s geographical reach extends across the country, its workforce is severely inadequate in light of the backlog of pending matters. ODR, which integrates video conferencing, digital evidence management, AI case management and online mediation tools, has the potential to increase the effective capacity of the NCLT considerably without the need to add physical facilities or judges. Certain categories of insolvency matters, such as claims submitted by creditors above a threshold amount, operational creditor disputes and questions of procedural compliance, are especially suited to resolution through ODR without compromising due process requirements.
The hybrid digital dispute resolution framework of Verma and Yadav is relevant here.20 Their model, which combines ODR with algorithmic accountability tools and human oversight at decision turning points, offers a blueprint that can be adapted to insolvency proceedings. The principle that technological efficiency must be balanced against procedural fairness and stakeholder access aligns well with the governance needs of insolvency law, in which the interests of several classes of creditors, resolution applicants and operational creditors must be reasonably accommodated.
D. AI-assisted case management in the NCLT
It is well documented that the NCLT has faced significant challenges in case management. The combination of thousands of pending applications, inadequate bench strength, frequent adjournments and the absence of a sophisticated case-tracking and prioritisation system produces a procedural environment that is inconsistent with the philosophy of the IBC, which envisaged the time-bound resolution of cases. AI-assisted case management systems that intelligently schedule hearings, prioritise urgent applications, identify redundant proceedings and highlight cases approaching statutory deadlines could improve the functioning of the NCLT without any amendment to the law.
Rafiq’s empirical investigation of the use of AI in Indian judicial processes is clear about its potential advantages for legal research, case management and document review in Indian courts. The Supreme Court’s own SUPACE, an AI-powered legal research and assistance tool, demonstrates the judiciary’s openness to the judicious use of technology. Extending similar tools to NCLT case management, tailored to insolvency procedures, would be both logical and institutionally appropriate.
Challenges and critical concerns
A. The problem of algorithmic transparency and due process
The application of AI to judicial processes raises significant questions of transparency and due process that cannot be overlooked in the pursuit of technological efficiency. In the legal context in particular, where all those affected have a legitimate right to understand how and why decisions have been made against them, the opacity of complex machine-learning models, the so-called black box problem, presents a real difficulty. Khokhar and co-authors make this argument forcefully:21 even the most sophisticated AI systems presently available are incapable of the contextual reasoning and interpretive judgment required for proper adjudication, particularly in relation to novel legal questions and exceptional factual circumstances.
This concern is especially acute in the insolvency environment. Resolution decisions affect thousands of creditors, the survival or liquidation of productive enterprises and the livelihoods of employees. A system that delegated such consequential decisions to an algorithm, even one demonstrating strong predictive accuracy, while affording few means to review and challenge that algorithm, would be constitutionally questionable and institutionally untenable.
B. Information asymmetry and data quality
The quality and completeness of the data on which AI systems are trained and applied are fundamental determinants of their effectiveness. Information asymmetry and inadequate information are known to be prevalent in the insolvency system, as observed in comparative studies of other jurisdictions. It is not always possible to extract complete details from annual corporate disclosures, which in India remain an imperfect process, and the under-reporting of informal transactions in the supply chain, together with limited credit bureau data for smaller businesses, restricts the information available to AI systems in insolvency.
Although the relevant scholarship draws on jurisdictions outside India, the patterns of data control and data enforcement observed in Nepal bear similarities to those in India. The call for the adoption of digital governance tools and AI-powered auditing22 is a valuable proposition, but it presupposes that the quality and availability of financial data are first improved, a step that Indian policymakers must explicitly pursue if AI tools are to support the insolvency framework effectively and efficiently.
C. Group insolvency and the coordination challenge
The group insolvency challenge identified by Raj and Tripathy23 is not a problem that AI alone can solve, but it is one that AI can help to manage to a substantial extent. In the absence of any uniform procedure for treating a group of companies, resolution professionals handling the resolution of interconnected corporate entities must conduct separate resolutions, administered by different benches and lacking any formal coordination. This produces value reductions through asset partitioning, the duplication of processes and competing judicial decisions.
Part of this coordination problem may be mitigated by using AI to coordinate case management systems, which can automatically detect and flag related proceedings or linked assets and liabilities across entities within a group and can facilitate the sharing of permissible information between resolution professionals and the relevant NCLT benches. This would not substitute for the legislative reform that Raj and Tripathy forcefully advocate, but it would offer an interim, pragmatic technological measure to minimise the worst features of the current situation.
D. Ethical and institutional capacity concerns
The volume edited by Subudhi, Das and Patra, titled Future of Research in Management and AI,24 presents a range of ethical and institutional issues that bear directly on the use of AI in insolvency governance. The contributors emphasise the risks that organisations face during transformation driven by the application of artificial intelligence, risks grounded in data governance, algorithmic bias, accountability gaps and a potential technological dependency that may erode institutional capacity.
In the Indian insolvency landscape, these considerations manifest in several practical challenges: how the IBBI and the NCLT will regulate and monitor the use of AI within their respective spheres of activity; whether resolution professionals will be trained to use and critically appraise AI outputs; and whether adequate mechanisms for redress will exist where AI-assisted decisions prove unfair. Addressing these institutional capacity deficits in a forward-looking manner is not optional; it is a precondition for the responsible use of AI in insolvency proceedings.
Towards a governance framework for AI-driven insolvency resolution
Drawing together the analytical threads of this paper, the study proposes a governance framework resting on four interrelated pillars.
The first pillar is the human-in-the-loop principle. All AI systems deployed across the insolvency process, from case management to document review, claims management and risk identification, should be designed to augment rather than replace human decision-making. Ultimate decision-making power should remain with competent judicial or quasi-judicial authorities, and parties should retain the right to challenge AI-generated outputs and to have any AI-dependent decision explained and confirmed by a human decision-maker.
The second pillar is transparency and explainability. AI systems in the insolvency domain must provide intelligible explanations for their outputs. This requires regulation that imposes explainability requirements on AI tools used within legal functions as a design consideration, together with disclosure obligations requiring resolution professionals and tribunals to declare where an AI tool has been applied in a particular determination.
The third pillar is data governance and data quality assurance. The IBBI should establish and maintain a comprehensive, standardised insolvency data repository to provide reliable, consistent and complete financial and procedural data to AI systems. Mandatory data standards for submissions by corporate debtors, together with integration with credit bureaus and MCA21, should be treated as basic requirements for the use of AI solutions and should be accompanied by periodic automated checks of data quality.
The fourth pillar is the development of institutional capacity. In order to use AI tools competently and critically, every NCLT member, resolution professional and IBBI official should receive recurrent technical training under a structured programme. This should be accompanied by the creation of an IBBI Technology Advisory Committee, composed of legal technologists, data scientists, insolvency practitioners and experts in judicial administration, to assist in developing and regularly assessing the AI systems used in the insolvency ecosystem.
Conclusion
The Insolvency and Bankruptcy Code, 2016 represented a genuine step towards transforming the resolution of corporate distress within the Indian framework. Yet the persistent problems of delay, pendency and institutional overload indicate that legislative action alone is only a small part of what is required to deliver the accessible, efficient and creditor-friendly insolvency platform that the Code envisaged. Mounting regulatory pressures, alongside political, economic and social pressures, have left the Indian judiciary increasingly stretched and in need of technology-based solutions to manage cases more intelligently. The substantial body of evidence presented in this study compellingly demonstrates that artificial intelligence, when applied appropriately, suitably constrained and properly controlled, can contribute significantly to enhancing resolution outcomes and judicial effectiveness in insolvency proceedings in India.
Predictive analytics has the potential to shift the insolvency culture towards a more value-preserving resolution process and earlier intervention. Automated document review can relieve the information burden on resolution professionals and enable broader claim processing. The introduction of ODR platforms offers multiple potential benefits for the effective capacity of the NCLT and for creditor access to proceedings. Within an already overwhelmed system of tribunals, AI-driven case management may introduce a measure of order, prioritisation and accountability.
At the same time, the concerns relating to agency capacity, data quality, due process and algorithmic opacity identified in this study are real and require attention. Comparative experience suggests that where AI is introduced too quickly or without careful consideration into a justice system, it often exacerbates existing inequalities rather than resolving them. India should draw on this lesson and integrate AI into insolvency governance with both ambition and diligence.
This paper proposes a governance framework that prioritises human oversight, transparency, data quality and institutional capacity for such integration. For the future of the insolvency resolution regime in India, judicial accountability and technological efficiency are not mutually exclusive. The task is to design systems that deliver both, as part of a more equitable and efficient insolvency system.
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Footnotes
1. The Insolvency and Bankruptcy Code, 2016, No. 31, Acts of Parliament, 2016 (India).
2. Insolvency & Bankr. Bd. of India, Annual Report 2023-24 (2024).
3. Janees Rafiq, Harnessing the Power of Artificial Intelligence in the Indian Justice System: An Empirical Study (2024).
4. Chandra Shekhar, Bailing Businesses, Boosting Banks: The Evolution of Insolvency and Bankruptcy Law in India (2025).
5. Prakriti Raj & Priyanshu Kumar Tripathy, Group Insolvency: Towards a Unified Framework for Corporate Distress (2025).
6. Janees Rafiq, Harnessing the Power of Artificial Intelligence in the Indian Justice System: An Empirical Study (2024).
7. Mathusha Francis, Srinath Perera, Wei Zhou & Samudaya Nanayakkara, Artificial Intelligence Applications for Proactive Dispute Management in the Construction Industry: A Systematic Literature Review (2025).
8. Nimisha Tambekar, Achieving Efficiency in the Indian Legal Field with Artificial Intelligence Tools (2024).
9. Muhammad Atif Khokhar, Attiya Anees & Muhammad Waqas Gujjar, From Judges to Algorithms: The Future of Corporate Adjudication in AI-Driven Courts (2025).
10. Vikas Verma & Ashok Yadav, Reimagining Consumer Redressal Mechanisms in AI-Driven Markets: The Case for Digital Mediation and Smart Regulation (2025).
11. Jyotirmoy Banerjee & Bhrigu Raj Maurya, A Critical Study on the Role of ODR in Insolvency Law with Reference to India and Vietnam in the Context of Global Health Transitions (2024).
12. Amin Elsayed Ahmed Lotfy, A Digital Accounting Framework for Enhancing Solvency and Financial Evaluation in Non-Bank Financial Institutions: Evidence from Egypt (2026).
13. G. Yoganandham, Trends, Challenges, and Opportunities in India’s Financial Sector: Policy Shifts, AI Integration, and Financial Stability—An Empirical Assessment (2025).
14. U.N. Comm’n on Int’l Trade Law, UNCITRAL Legislative Guide on Insolvency Law (2005).
15. Amin Elsayed Ahmed Lotfy, A Digital Accounting Framework for Enhancing Solvency and Financial Evaluation in Non-Bank Financial Institutions: Evidence from Egypt (2026).
16. G. Yoganandham, Trends, Challenges, and Opportunities in India’s Financial Sector: Policy Shifts, AI Integration, and Financial Stability—An Empirical Assessment (2025).
17. Nimisha Tambekar, Achieving Efficiency in the Indian Legal Field with Artificial Intelligence Tools (2024).
18. Mathusha Francis, Srinath Perera, Wei Zhou & Samudaya Nanayakkara, Artificial Intelligence Applications for Proactive Dispute Management in the Construction Industry: A Systematic Literature Review (2025).
19. Jyotirmoy Banerjee & Bhrigu Raj Maurya, A Critical Study on the Role of ODR in Insolvency Law with Reference to India and Vietnam in the Context of Global Health Transitions (2024).
20. Vikas Verma & Ashok Yadav, Reimagining Consumer Redressal Mechanisms in AI-Driven Markets: The Case for Digital Mediation and Smart Regulation (2025).
21. Muhammad Atif Khokhar, Attiya Anees & Muhammad Waqas Gujjar, From Judges to Algorithms: The Future of Corporate Adjudication in AI-Driven Courts (2025).
22. Prajwal Bhattarai, Global Corporate Practices and Insolvency Framework in Nepal: Analysis of Companies Act 2063 and Securities Act 2063 (2025).
23. Prakriti Raj & Priyanshu Kumar Tripathy, Group Insolvency: Towards a Unified Framework for Corporate Distress (2025).
24. Rabi Narayan Subudhi, Saumendra Das & Anita Patra eds., Future of Research in Management and AI (2025).