• Artificial intelligence (AI) has gained substantial traction in legal fields, including insolvency, particularly in developed nations such as the United States and the European Union. AI-powered tools like Data 61, DataLex AI, and ROSS have demonstrated their effectiveness in advanced analytics, scenario modeling, and financial performance predictions, significantly aiding insolvency law enforcement. For instance, LDM Global’s AI tool “Accelerator” has enhanced efficiency in insolvency proceedings, showcasing the transformative potential of AI in this domain.
• Globally, countries are actively integrating AI into insolvency frameworks to improve accuracy and efficiency. Portugal’s Citius platform streamlines communication between parties and courts, while Finland’s KOSTI platform enhances case management. Colombia has taken significant strides by authorizing AI use in its insolvency portal (MI), automating decision-making and expediting processes through a government decree introduced in 2021. Similarly, the UK’s National AI Strategy supports AI adoption in law enforcement, including insolvency-related matters.
• These advancements highlight a global trend toward leveraging AI to enhance insolvency law enforcement. India must follow suit by mandating the integration of AI agents into the Insolvency and Bankruptcy Code (IBC) framework. By adopting AI tools, India can address persistent challenges such as delayed resolutions, fraudulent activities, and the overwhelming volume of financial data that professionals must analyze. AI can streamline compliance processes, automate documentation, detect anomalies, and improve stakeholder communication. Additionally, predictive modeling can help forecast recovery outcomes and enhance asset valuation accuracy.
• AI’s integration would also empower insolvency professionals by automating routine tasks like document review and fraud detection while enabling them to focus on strategic decision-making. Generative AI platforms can analyze vast datasets to uncover hidden patterns or connections that might otherwise go unnoticed, increasing recoveries for creditors and improving transparency.
• In conclusion, mandating AI adoption within India’s IBC framework will align the country with global best practices while strengthening its insolvency ecosystem. This transformation will not only expedite resolutions but also foster greater transparency and reliability in outcomes—ultimately boosting investor confidence and contributing to economic stability. By embracing cutting-edge technologies, India can pave the way for a more efficient and equitable insolvency process that benefits all stakeholders involved. However, ethical considerations must be addressed to ensure fairness and accountability in AI-driven processes. Regulatory bodies should establish robust guidelines for algorithmic transparency and data privacy to safeguard stakeholder interests.
• Globally, jurisdictions like Singapore and the UK are leading by example in integrating AI into insolvency frameworks. Singapore employs AI for insolvency assessments under its Legal Technology Vision, while the UK uses automated platforms for case tracking. Similarly, US bankruptcy courts utilize AI tools for fraud detection.

  • India can benefit from these precedents by adopting a robust legal framework for AI integration in insolvency proceedings. This would involve establishing standards for algorithmic transparency and accountability while addressing ethical considerations like data privacy.

• Corporate Insolvency Resolution and Liquidation processes are inherently complex, involving intricate legal, regulatory, and financial challenges. These processes under the Insolvency and Bankruptcy Code (IBC) aim to streamline the resolution of distressed companies efficiently. However, insolvency professionals often face overwhelming workloads due to regulatory filings, legal queries, claims management, and stakeholder coordination. IBC stakeholders continue to manipulate the resolution processes and engage in various ‘games’ in order to protect their interests.
• Suspended directors or corporate debtors often engage in various tactics to obstruct the insolvency resolution process. They continue to conceal their assets or transfer assets to diminish the value of the estate, making it harder to realise and maximise value of the CD. Filing of frivolous litigations is a common tactic in order to prolong the processes. They attempt to undermine the role of the insolvency professional, by means like non-cooperation to disrupt the structured resolution process.
• Resolution applicants have been found to collaborate with other co-applicants in order to submit lowball offers in their resolution plans and trying to push the CD to Liquidation, with an aim to acquire control of the distressed company at minimal cost. Inclusion of ambiguous clauses in the resolution plans is also a common practice, leaving room for interpretation to be exploited during plan implementation. They have been found to resort to unnecessary litigation after plan approval, using legal tactics to delay implementation and gain leverage in plan implementation.
• Creditors have also been found to engage in ‘strategic-games’ in order to maximize their recoveries or influence outcomes. Such tactics include claim inflation, where penalties or exaggerated amounts are added to their claims to secure higher payouts, manipulation of their status by misrepresenting unsecured claims as secured to gain priority in the resolution process. Delay in the submission of claim documents is another strategy often used to avoid thorough scrutiny and potential rejection of inflated claims. Additionally, creditors have been found lobbing with other members of the Committee of Creditors (CoC) to sway decisions in their favour, potentially compromising the fairness and transparency of the process.
• Forensic Auditors have been found to be influenced by erstwhile management of the CD to align their findings with management's desired outcomes. They have been found to provide selective reporting, where unfavourable information is intentionally concealed from the Committee of Creditors (CoC), thereby hindering informed decision-making and compromising the transparency and fairness of the process.
• Registered Valuers have been found to manipulate asset valuations, undervaluing or overvaluing assets to favour particular creditors or resolution applicants. In some cases, the valuation process has been intentionally delayed, stalling the progress of the resolution. Additionally, asset valuers might collude with certain stakeholder, providing appraisals that disproportionately benefit those those stakeholders, thereby compromising the impartiality and integrity of the valuation process.
• Although several safeguards have been devised by the Insolvency and Bankruptcy Board of India (IBBI) over the past several years - such as regulatory oversight by the National Company Law Tribunal (NCLT), transparency requirements, and a code of conduct for insolvency professionals and Committee of Creditors (CoC) members—these measures have their limitations. Despite these safeguards, stakeholders continue to exploit loopholes and game the system, employing tactics that delay or derail the insolvency resolution process.
• These persistent challenges highlight the need for advanced solutions, and technologies like AI agents to address many of these inefficiencies. AI agents can enhance transparency, reduce biases, and improve the speed and accuracy of critical tasks such as data analysis, asset valuation, fraud detection, and workflow management. By leveraging these technologies, the insolvency resolution process can become more efficient, equitable, and resistant to manipulation, ensuring successful outcomes for all stakeholders involved.

AI agents in the context of insolvency processes can be categorized into two main types: Generative Retrieval-Augmented Generation (RAG) Agents and Action Agents, each serving distinct but complementary roles to streamline and enhance insolvency workflows.

Generative RAG agents combine retrieval systems with generative AI to provide accurate, contextually relevant responses grounded in insolvency-specific knowledge. These agents retrieve relevant data from external sources, such as case laws, creditor claims, regulatory guidelines, or financial records, and integrate it into their generated responses. This ensures that outputs are both factually accurate and tailored to the complexities of the Insolvency and Bankruptcy Code (IBC) framework. For example, a RAG agent could assist insolvency professionals by summarizing voluminous legal judgments, extracting key provisions from the IBC, or generating draft resolution plans based on retrieved precedents and templates.

• Applications in Insolvency:
  • Automating legal research by retrieving and summarizing case laws.
  • Answering stakeholder queries about procedural timelines or compliance requirements.
  • Simulating outcomes of different resolution plans using historical data.

Action agents are autonomous systems designed to execute tasks, make decisions, and manage workflows dynamically during insolvency processes. Unlike RAG agents, which focus on generating information, action agents interact with their environment to perform multi-step tasks such as managing creditor claims, coordinating stakeholder communications, or tracking compliance deadlines. These agents adapt to changing conditions and provide real-time support for complex operations like pre-packaged insolvency resolution processes (PPIRP) or liquidation proceedings.

• Applications in Insolvency:
  • Automating claim verification and classification for creditors.
  • Monitoring procedural deadlines and ensuring adherence to statutory requirements.
  • Facilitating negotiations during one-time settlements by analyzing financial data and suggesting optimal terms.

In summary, Generative RAG agents excel at providing accurate information and insights for decision-making in insolvency processes, while Action Agents bring autonomy and adaptability to execute complex workflows. Together, these AI agents can revolutionize insolvency management by improving efficiency, reducing administrative burdens, and enhancing transparency across all stages of the IBC framework.

Currently, insolvency professionals are leveraging generative AI applications like ChatGPT to summarize voluminous legal judgments and enhance legal research by identifying relevant case laws and statutory provisions. However, the strategic deployment of AI remains limited to these functions. A broader integration of AI could include advanced case management systems that track timelines and compliance requirements or valuation models that set optimal asset prices during liquidation.

ChatGPT and ChatIBC differ primarily in their scope, specialization, and application within the insolvency domain:

• Specialization: ChatIBC is a specialized AI model focused exclusively on the Insolvency and Bankruptcy Code (IBC) 2016 and related insolvency processes. It is trained on insolvency-specific datasets, including case laws, regulatory guidelines, and financial data relevant to insolvency resolution. ChatGPT, by contrast, is a general-purpose generative AI model designed to handle a wide range of topics and tasks but lacks domain-specific training in insolvency[1].
• Functionality and Use Cases: ChatIBC supports insolvency professionals by automating tasks such as drafting resolution plans, analyzing creditor claims, detecting fraud, and forecasting insolvency risks. It can be customized with firm-specific documents and workflows to provide tailored assistance. ChatGPT is often used for summarizing legal judgments, basic legal research, and answering general queries but does not offer the same depth of domain-specific functionality or customization for insolvency workflows[1].
• Accuracy and Contextual Understanding: Due to its focused training, ChatIBC provides more accurate, context-aware responses related to insolvency matters, including procedural compliance and regulatory nuances. ChatGPT offers flexible and natural language responses but may be less reliable for complex, specialized insolvency issues without additional fine-tuning or customization[1].
• Integration and Customization: ChatIBC can be integrated into insolvency professionals’ workflows with customization options that reflect firm-specific practices and regulatory requirements. ChatGPT requires more sophisticated integration efforts and is less tailored out-of-the-box for insolvency-specific tasks, though it can be adapted with custom prompts or fine-tuning[1].

In summary, ChatIBC is a domain-specific AI agent designed to enhance efficiency, accuracy, and decision-making in insolvency processes under the IBC, while ChatGPT serves as a versatile, general-purpose AI tool with broader but less specialized capabilities.

• ChatIBC is a specialized AI model designed specifically for the Insolvency and Bankruptcy Code (IBC) 2016 domain, much like how Harvey AI is tailored for the legal field. ChatIBC is trained on extensive insolvency and bankruptcy datasets, including case laws, resolution plans, and regulatory guidelines issued by the Insolvency and Bankruptcy Board of India (IBBI). This focused training enables ChatIBC to understand the nuances of insolvency proceedings, resolution frameworks, and liquidation processes, providing precise and context-aware assistance for professionals working within the insolvency ecosystem.
• Similar to Harvey AI’s customization for law firms, ChatIBC can be fine-tuned with firm-specific insolvency documents, templates, and workflows. Insolvency professionals and resolution applicants can leverage this customization to receive tailored support in drafting resolution plans, analyzing creditor claims, and navigating complex procedural requirements under IBC 2016. By integrating ChatIBC into their workflows, insolvency practitioners can enhance efficiency, reduce errors, and ensure compliance with the latest regulatory updates and judicial precedents, all while maintaining data security and confidentiality.
• Moreover, ChatIBC offers advanced capabilities such as predictive analytics for insolvency outcomes, automated summarization of lengthy insolvency petitions, and multilingual support for diverse jurisdictions within India. These features empower insolvency professionals to make informed decisions, streamline due diligence, and manage multi-stakeholder communications effectively. By focusing exclusively on insolvency and bankruptcy, ChatIBC fills a critical gap that general-purpose AI models cannot address with the same depth and accuracy, making it an indispensable tool for the insolvency domain.

Pre-insolvency agents play a vital role at the negotiation stage for one-time settlements with banks and creditors, facilitating early detection and prediction of insolvency risks, and supporting pre-pack resolution processes under the Insolvency and Bankruptcy Code (IBC). Here’s how these agents contribute across these key areas:

• Negotiation and One-Time Settlement Facilitation: During the pre-initiation or pre-insolvency phase, agents assist the corporate debtor (CD) and creditors in confidentially exploring restructuring options and negotiating settlements to resolve financial stress without triggering formal insolvency proceedings. This informal stage allows swift discussions with unrelated financial creditors or operational creditors to seek approval for resolution plans or settlements, helping avoid lengthy litigation and preserve business value[1][2][4].
• Early Detection and Prediction of Insolvency: Pre-insolvency agents leverage financial data analysis and predictive models to identify early warning signs of distress, such as cash flow issues or overleveraged balance sheets. This enables proactive intervention before formal insolvency arises, allowing debtors and creditors to implement corrective measures or restructuring strategies. Such early detection is crucial for minimizing losses and improving recovery prospects[4][7].
• Support for Pre-Packaged Insolvency Resolution Process (PPIRP): In the pre-pack process, agents help manage the hybrid informal-formal structure where the pre-initiation phase focuses on negotiation and plan formulation, and the post-initiation phase formalizes the resolution with statutory protections. Agents coordinate meetings of creditors, assist in preparing and evaluating base resolution plans, and facilitate competitive bidding among resolution applicants. This accelerates the resolution timeline (typically within 120 days) and enhances value maximization for stakeholders[1][3].

Overall, pre-insolvency agents act as facilitators, analysts, and coordinators who help streamline negotiations, enable early risk identification, and support efficient resolution planning, thereby reducing the likelihood of full insolvency and promoting smoother, faster recovery under the IBC framework.

• AI agents can be categorized into distinct groups based on their roles and functionalities, offering specialized capabilities to enhance efficiency, accuracy, and decision-making in insolvency processes. These categories include Legal Agents, Process Agents, Planning Agents, Marketing Agents, Fraud Detection Agents, and Prediction Agents. Each type of agent addresses specific challenges within the insolvency framework, streamlining operations and improving outcomes.
• Document Agents play a pivotal role in reducing the manual workload associated with drafting key documents such as Resolution Plans, Progress Reports, and Compliance Filings. By ensuring adherence to statutory language and minimizing human errors, these tools save time and streamline legal documentation. Generative AI can also assist in drafting responses to objections raised by stakeholders, ensuring precision and consistency in legal communication. Beyond drafting, Document Agents can automate the review of large volumes of legal documents, extracting critical information and summarizing it for quick reference, thereby expediting decision-making.
• Workflow Agents play a crucial role in enhancing coordination among teams involved in the insolvency process. These agents can assign tasks to relevant team members, send reminders about upcoming deadlines, and provide real-time updates on case progress. By automating repetitive workflow tasks, these agents help professionals stay organized and focused, ensuring that every aspect of the resolution process is handled in a timely and efficient manner.
• Planning Agents are pivotal in the insolvency process, as they can be tasked with developing and evaluating resolution plans. AI-driven tools in this category analyze creditor claims, available resources, and legal constraints to design optimal repayment schedules and restructuring plans. These agents enable resolution professionals to balance the interests of various stakeholders, including creditors, shareholders, and employees, while adhering to legal and financial constraints.
• Marketing Agents play a vital role in enhancing stakeholder communication within the insolvency process. These agents utilize personalized, AI-driven communication strategies to provide timely updates to creditors, investors, and regulators. By automating and streamlining communication efforts, they reduce manual effort while maintaining clarity and professionalism in all interactions.
• Fraud Detection Agents are crucial in the insolvency process, as they focus on identifying potential fraud such as Preferential, Undervalued, Fraudulent, and Extortionate (PUFE) transactions, concealed assets, or other irregularities in financial records. AI tools in this category are capable of scanning extensive financial data to detect anomalies and hidden patterns that may indicate fraudulent activities.
• Asset Valuation Agents play a crucial role in the insolvency process by providing accurate and unbiased assessments of a corporate debtor’s assets. These agents are essential for ensuring that stakeholders, particularly the Committee of Creditors (CoC), have reliable information to make informed decisions about resolution plans. By conducting thorough valuations, they help stakeholders understand the true worth of the assets, which is critical for negotiating resolution plans and determining the viability of potential restructuring strategies.
• Prediction Agents are invaluable tools in the insolvency process, offering decision support and forecasting recovery outcomes. By utilizing advanced AI models, these agents analyze historical insolvency data, financial records, and case-specific variables to predict creditor recovery rates and timelines. This predictive capability provides stakeholders with realistic expectations and enables resolution professionals to plan effectively, ensuring that all parties are well-prepared for potential outcomes.
• Process Agents are instrumental in modernizing the insolvency process by monitoring compliance and facilitating collaborative workflows. These agents play a crucial role in ensuring that regulatory deadlines are met by tracking timelines, ensuring filings are completed on schedule, and promptly alerting stakeholders to changes in laws or guidelines. By minimizing the risks of non-compliance, Process Agents ensure that all legal and procedural requirements are adhered to efficiently, thereby safeguarding the integrity of the insolvency process.

In conclusion, IBCagents and ChatIBC represent transformative advancements in the insolvency ecosystem, harnessing AI to address the complexities inherent in the Insolvency and Bankruptcy Code (IBC) processes. By automating routine tasks, enhancing data analysis, and providing predictive insights, these AI agents significantly reduce the administrative burden on insolvency professionals while improving accuracy and timeliness. Their ability to facilitate stakeholder coordination, detect fraud, and optimize resolution strategies aligns perfectly with the IBC’s objective of maximizing asset value and expediting corporate revival or liquidation. As India continues to refine its insolvency framework with regulatory reforms and embraces technological innovation, the strategic integration of AI-powered tools like IBCagents and ChatIBC will be pivotal in creating a more efficient, transparent, and equitable insolvency resolution landscape.

To fully realize this potential, there is an urgent need to develop a dedicated, multilingual AI model tailored specifically for the Indian insolvency context, capable of understanding diverse languages and jurisdictional nuances across the country. Furthermore, establishing an Innovation Hub focused on insolvency technology can foster a vibrant startup culture, encouraging continuous research, development, and deployment of cutting-edge AI solutions. Such initiatives will not only accelerate the modernization of insolvency processes but also position India as a global leader in leveraging AI for legal and financial reforms, ultimately benefiting all stakeholders in the insolvency ecosystem.