Epistemic Contamination in Corporate Insolvency: AI Hallucinations under the Bharatiya Sakshya Adhiniyam

The Bharatiya Sakshya Adhiniyam (‘BSA’), India’s newly enacted evidence code, which overhauls the Indian Evidence Act, 1872, was designed to secure the integrity of digital records in the courtroom. Yet, within the realm of corporate insolvency, this statutory modernization inadvertently masks a profound epistemic risk. As resolution professionals increasingly deploy artificial intelligence to trace assets under India's Insolvency and Bankruptcy Code, 2016 (‘IBC’), the National Company Law Tribunal (‘NCLT’), the nation’s primary adjudicatory forum for corporate insolvency cases, is confronted with the phenomenon of algorithmic hallucinations. This post argues that while the BSA effectively authenticates the physical container of digital evidence, its failure to scrutinize the underlying machine-learning logic creates a dangerous veneer of forensic rigour, that threatens to ground high-stakes liability determinations on synthetic data rather than commercial reality.

The Evidentiary Transplant: AI in Asset Tracing

The mechanics of modern corporate restructuring necessitate a rigorous autopsy of a debtor's financial history. Under the IBC, the adjudication of avoidance applications, encompassing preferential, undervalued, and fraudulent transactions, requires courts to parse complex, often transnational webs of capital-routing and shell entities. Recognizing that manual ledger verification is functionally obsolete in the face of large-scale corporate collapses, liquidators now routinely deploy automated AI-driven forensic tools to satisfy stringent statutory timelines. These predictive models are tasked with ingesting terabytes of unstructured data, from cross-border banking ledgers to internal corporate communications, to detect financial anomalies and infer hidden transactional linkages.

However, a critical shift has occurred in how this technology is utilized in the courtroom.  As recent scholarship on the digital transformation of forensic accounting confirms, algorithmic output has transitioned from serving merely as preliminary investigative intelligence to functioning as the substantive evidentiary bedrock of avoidance claims, increasingly building the formal technical evidence utilized in judicial processes. Much like an ill-suited regulatory transplant, the uncritical importation of predictive technology into the adjudicatory space fundamentally alters the nature of proof. In contemporary insolvency litigation before the NCLT, the algorithm effectively constructs the commercial narrative, increasingly replacing human forensic judgment with computational inevitability.

The Mechanics of ‘Synthetic Liability’

The fundamental flaw in treating algorithmic outputs as objective truth lies in the nature of machine learning itself. Unlike traditional, deterministic forensic software which executes rigid, rule-based queries such as isolating all transfers above a specific monetary threshold, AI-driven tools are probabilistic. They operate by inferring relationships, clustering anomalies, and predicting patterns based on their underlying training data.

In the chaotic environment of a corporate collapse, this probabilistic nature becomes a profound vulnerability. Distressed companies rarely leave behind clean, well-annotated financial records; their data is often fragmented, disorganized, or deliberately obfuscated by erstwhile management. When an algorithmic model is forced to bridge gaps in these incomplete datasets, it is highly susceptible to ‘hallucinations’ fabricating non-existent transaction linkages or mischaracterizing routine, legitimate commercial activity as fraudulent round-tripping due to over-fitting or proximity bias.

 This introduces the risks associated with synthetic evidence which, when relied upon for corporate avoidance claims, creates a form of ‘synthetic liability’, into the courtroom. If a liquidator submits a hallucinated forensic report as the primary basis for an avoidance application, the burden of justification effectively flips. Suspended directors and third-party vendors are thrust into an epistemic trap: they are tasked with proving a negative, forced to dismantle a complex, algorithmically generated fiction to demonstrate to the tribunal that a mathematically inferred conspiracy did not actually occur.

The BSA’s Epistemic Blindspot

The BSA attempts to fortify the integrity of digital evidence, yet its framework commits a fundamental error when applied to predictive technology. The statute’s provisions governing electronic records, primarily section 63, which succeeds the legacy section 65B of the Indian Evidence Act, mandate a rigorous certification regime. To admit an electronic record, such as an AI-generated forensic report, a deponent must affirm that the computer or device generating the record was operating properly and remained free from external tampering during the material period.

However, this statutory mechanism creates a perilous epistemic blind spot. The BSA is designed exclusively to authenticate the physical and digital container, remaining entirely agnostic to the computational reasoning. A compliance certificate merely confirms that the server hosting the forensic software was not hacked or subjected to data corruption; it offers absolutely zero scrutiny of the proprietary neural network that produced the substantive financial findings.

This distinction is critical. A system can function flawlessly from a hardware perspective while executing a profoundly biased or hallucinating algorithmic model. By equating hardware hygiene with substantive reliability, the current statutory framework provides a dangerous illusion of evidentiary security. Much like an ill-fitting corporate code transplanted into an incompatible sector, the BSA provides a mere veneer of rigour when applied to artificial intelligence. It allows synthetic, mathematically hallucinated data to pass smoothly through the tribunal's gates, effectively insulating proprietary code from necessary judicial oversight.

Conclusion: Reclaiming Institutional Contestability

The NCLT is fundamentally a commercial adjudicatory body, not a technological auditor. However, as the volume and complexity of AI-driven evidence continue to escalate, tribunals can no longer afford to accept algorithmic outputs as infallible simply because they bear a valid certificate of technical compliance under section 63 of the BSA. To concede that an algorithmic output is inherently objective, without substantive interrogation, is to surrender the rule of law to a form of technocratic absolutism.

To prevent corporate debt restructuring from being steered by synthetic narratives, the procedural safeguards within insolvency litigation must adapt to pierce the veil of proprietary code. Firstly, tribunals must establish clear protocols for algorithmic discovery. When a suspended director or affected stakeholder challenges a forensic report on the grounds of epistemic contamination, legal representatives must be permitted to cross-examine the parameters, error rates, and training datasets of the deployed tools. Furthermore, the definition of a forensic 'expert' must evolve. Traditional chartered accountants, while commercially astute, may lack the technical literacy required to defend or dismantle 'black-box' neural networks. The NCLT will increasingly need to rely on independent digital data scientists to evaluate whether a forensic report reflects objective financial reality or merely algorithmic bias.

Ultimately, the BSA provides a robust legal shield against the external manipulation of electronic records, but it offers no defense against the internal cognitive errors of the systems analyzing them. If the NCLT treats hardware certification as a proxy for algorithmic truth, corporate debt restructuring risks being steered not by financial reality, but by synthetic courtroom narratives. Unless bankruptcy courts actively reclaim the mechanism of contestability and subject algorithmic logic to human jurisprudence, the pursuit of commercial justice risks being compromised by the very technology deployed to secure it.

Saurabh Ranka is an advocate practicing before the Supreme Court of India and the High Court of Rajasthan.