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Mining the Machine: Building a Data Architecture for India’s Insolvency Ecosystem

I. Abstract

India’s insolvency infrastructure generates a huge amount of information, but most of it sits scattered across government websites in formats that are difficult to analyse. This blog is set out to bridge that gap. By combining browser automation, HTML parsing, and automated document retrieval, it shows how raw tribunal records from the NCLT and IBBI can be transformed into a structured, research‑ready dataset. Instead of just capturing basic case information, the approach pieces together the entire procedural journey of insolvency cases, such as hearings, filings, interim applications, connected matters and turns them into meaningful data for empirical research.

II. The Problem of Institutional Data Scarcity

India’s Insolvency and Bankruptcy Code, 2016 was built on a single foundational premise: that creditor confidence depends on predictability. Investors must be able to look at Researchers studying India’s insolvency system run into a simple but stubborn problem: the institutions whose performance we want to measure are the same ones that hold the relevant data. The NCLT and the IBBI do publish their records publicly, but they are meant for interested parties to look up individual cases, not for statistical analysis. There’s no API, no bulk‑download option but just page‑by‑page searching.
The result is an odd mismatch: the information exists, but not in a form that anyone can easily analyse at scale. This study treats these websites as if they were databases in disguise and builds a method to extract the required information systematically.
The lack of structured datasets in the insolvency ecosystem has been noted by several researchers. Early empirical work by Chatterjee, Shaikh and Zaveri (2017) found that the information systems of the National Company Law Tribunal do not provide a complete lifecycle view of insolvency cases, with key variables such as filing dates, hearing dates and claim amounts often missing from published orders. Similar concerns regarding data fragmentation in judicial and quasi-judicial tribunals have been raised in earlier studies of India’s debt recovery framework (Ravi 2015; Regy & Roy 2017). More recent scholarship on insolvency reforms also emphasises that without systematic data publication, regulators and researchers cannot effectively evaluate institutional performance or policy outcomes (Mokal et al. 2022). These gaps force empirical researchers to construct datasets directly from administrative records through techniques such as automated web scraping and document extraction.
a distressed company and form a reasonable estimate of how much they will recover, and how long recovery will take. The National Company Law Tribunal (NCLT), operationalised on 1 June 2016, is the adjudicating authority through which that predictability is supposed to be delivered.

Evaluating whether the NCLT is delivering on that promise ought to be a straightforward empirical exercise. It is not. As DAKSH observed, both institutions “operate as black-boxes, and their functioning can be difficult to understand, not just for litigants, but even for the lawyers who argue before them.”

III. Understanding Where the Data Lives

The first step was simply mapping the ecosystem: figuring out where different pieces of insolvency‑related information are stored.
NCLT Case Database
The NCLT website contains details such as filing and registration dates, case numbers, party names, advocate details, case status, hearing histories, interlocutory applications, and connected matters. But because the site depends heavily on interactive elements, you can’t scrape it with a simple HTML parser. Navigating even a few hundred pages manually would be impossible, which is why automation becomes essential.
IBBI Order Repository
The IBBI publishes regulatory orders, including key milestones that the NCLT summaries don’t capture, for example, resolution approvals or important developments in creditors’ committee proceedings. These documents are usually PDFs, so extracting structured data from them requires separate tools.

IV. Automating What Humans Can’t Do

To gather case‑level information from the NCLT, the study uses Selenium, not just to scrape the HTML, but to mimic human interaction with the website. The script can click through paginated search results, open individual cases, shift between tabs, and pull information from tables that would otherwise stay hidden.

The automated pipeline performs tasks such as:

  • going through hundreds of paginated results
  • opening each case and extracting details
  • parsing tables for dates, parties, advocates, and statuses
  • appending everything into structured datasets

Instead of viewing the website as a static page, the script treats it like a machine to be operated repeatedly, systematically, and reliably.

V. Rebuilding Cases as Procedural Timelines

The aim isn’t just to collect snapshots of cases, but to reconstruct the full sequence of events inside each one.

Listing histories tell us every date a case was scheduled on before the tribunal, making it possible to study the frequency of hearings, gaps between listings, and periods of inactivity.

Interlocutory applications (IAs/MAs) reveal how procedurally complex a case is. Their number and timing can signal how contentious or resource‑intensive a matter has become.

Connected matters show how many related cases cluster around the same dispute. This opens the door to network‑level analysis instead of treating each case as isolated.

VI. Capturing Resolution Documents from the IBBI

Some key insolvency milestones exist only in IBBI orders. A second pipeline pulls these documents using company CINs and automatically downloads them. Once retrieved, the documents can be processed to identify events such as committee approvals or final resolutions, details that typically don’t appear on the NCLT portal.

A Four‑Stage Data Pipeline

The entire architecture follows a clean, modular process:

  1. Data Acquisition – automated scraping through browser automation
  2. Data Parsing – extracting structured fields from HTML and documents
  3. Data Storage – saving everything in spreadsheets or dataframes
  4. Incremental Saving – protecting progress during long scraping runs

At the end, the system produces linked datasets covering case metadata, hearings, applications, connected matters, and regulatory orders.

VII. Why This Matters for Legal Research

Most institutional datasets in India don’t exist in ready‑made form, especially in the legal and regulatory domain. But that doesn’t mean the data isn’t there. It just means researchers need to build the bridge themselves.

This methodology shows how to convert administrative transparency into usable research infrastructure. By structuring dispersed records, researchers can finally answer empirical questions such as:

  • How often are cases listed?
  • How does procedural complexity affect the duration of cases?
  • How do tribunal workloads influence delays?

These are not abstract questions. Each delay affects creditor recoveries and the value of distressed assets.

VIII. Policy implication

A key policy implication that emerges from this exercise is the need for a more structured and accessible data architecture within India’s insolvency framework. Although significant information is publicly available through institutions such as the National Company Law Tribunal and the Insolvency and Bankruptcy Board of India, it is largely presented for individual case reference rather than for systematic analysis. The absence of machine-readable datasets, bulk downloads, or programmatic access means that researchers and policymakers must reconstruct datasets manually. Establishing a centralised insolvency data portal that publishes structured information on filings, hearings, interim applications, and case outcomes would make existing transparency far more meaningful and usable.

Another important step would be improving interoperability across institutions involved in insolvency proceedings. A single matter often appears across multiple platforms, including the National Company Law Tribunal, the Insolvency and Bankruptcy Board of India, and the National Company Law Appellate Tribunal, without a unified identifier linking related records. Introducing standardised case identifiers and publishing basic structured metadata alongside orders and regulatory documents would allow procedural histories to be traced more easily and reduce the need for manual reconciliation of records.

More broadly, strengthening institutional data systems should be seen as part of the regulatory infrastructure supporting the Insolvency and Bankruptcy Code, 2016. Reliable and accessible datasets would enable independent evaluation of case duration, tribunal workloads, and recovery outcomes—issues that directly affect the effectiveness of the insolvency regime. Improving data accessibility would therefore not only assist research but also contribute to more evidence-based policy development in India’s insolvency ecosystem.

IX. Conclusion

India’s insolvency ecosystem wasn’t designed for empirical research, but with the right tools, it contains enough information to support it. By combining automation, event‑tracking, and cross‑institutional document retrieval, this approach reconstructs insolvency cases as dynamic processes rather than static files.

In doing so, it expands what empirical legal research can measure and analyse and shows that even without official datasets, researchers can still build the data infrastructure they need.

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