New Delhi: 05 Sep 2026 I A controversial insolvency case involving Essel Group founder Subhash Chandra has triggered a much bigger question for India’s financial and legal ecosystem: Can technology—and particularly Artificial Intelligence—make complex insolvency cases more transparent, faster and harder to manipulate?
First, an important clarification: the widely circulated claim that the NCLT has simply written off ₹22,000 crore of bank loans is misleading. The approximately ₹22,006 crore figure represents claims admitted against Chandra in his capacity as a personal guarantor for loans taken by Essel/Zee-linked companies—not ₹22,000 crore personally borrowed by him. The principal corporate borrowers remain liable, and creditors retain recovery avenues.
An earlier repayment plan involving approximately ₹6.25 crore from Chandra’s personal estate against the admitted claims became the centre of intense debate. More recently, an NCLT special bench stayed that repayment plan and barred the alienation of his assets, adding another layer to the ongoing legal battle.
But here’s where the AI story begins.
Cases involving large corporate groups can involve thousands of transactions, multiple companies, guarantees, related-party relationships, assets spread across jurisdictions, changing valuations and years of financial records.
For human investigators, connecting all those dots can be enormously time-consuming.
AI could fundamentally change that equation.
What could AI do in future insolvency cases?
1. Trace the money trail
AI systems could analyse millions of transactions and identify unusual fund movements, circular transactions, related-party transfers and unexplained changes in financial positions.
2. Build a real-time corporate relationship map
Instead of manually examining corporate structures, AI could map relationships between companies, directors, shareholders, lenders, guarantors and related entities—helping investigators see the entire ecosystem.
3. Detect hidden or undervalued assets
AI-powered financial intelligence could compare declared assets against historical transactions, property records, market information and corporate disclosures to flag inconsistencies for human investigation.
4. Identify suspicious patterns earlier
Machine-learning models could flag unusual borrowing, rapid asset transfers, repeated guarantees or other patterns that may deserve closer scrutiny before a company reaches a full-blown insolvency crisis.
5. Give creditors better decision support
AI could simulate different recovery scenarios—such as liquidation, restructuring or repayment plans—and estimate potential outcomes based on available evidence.
The bigger question: Can AI make insolvency more transparent?
The answer could be yes—but AI should assist the process, not replace judges, insolvency professionals, investigators or creditors.
AI can identify patterns.
AI can connect data.
AI can raise red flags.
But human experts must determine what those signals actually mean and whether they have legal significance.
The Subhash Chandra case demonstrates just how complicated the distinction between corporate debt, personal guarantees, creditor claims and actual recoverable assets can become.
Tomorrow’s Insolvency System May Look Very Different
Imagine an insolvency platform where every major financial transaction is continuously analysed, corporate relationships are mapped automatically, asset movements trigger alerts, and investigators receive an AI-generated risk map before beginning a forensic examination.
That future isn’t simply about recovering more money.
It is about creating a financial ecosystem where transparency happens earlier—not after billions of rupees are already at risk.
The real lesson from the ₹22,000-crore controversy may therefore not be about a “write-off.”
It may be about something bigger: In the future, AI could make it much harder for financial complexity to hide the truth.
Alka Sachdeva, Author
Where Technology Meets the Future of Business.
