AI Hallucination and Insurance: The Emerging Liability Risk Indian Corporates Must Understand
Artificial Intelligence is no longer experimental. It is embedded in underwriting models, financial forecasting, HR
screening, legal drafting, healthcare diagnostics, supply chain optimisation and customer service systems across
India.
Boards are approving AI budgets. Management teams are accelerating deployment. Vendors are promising efficiency
gains.
But there is one uncomfortable truth that deserves attention: AI does not always know when it is wrong.
And when it is wrong, the consequences can be real.
This is where the concept of AI hallucination becomes relevant; not as a technical term, but as a
liability risk.
What Exactly Is AI Hallucination?
AI hallucination occurs when an AI system generates incorrect, fabricated or misleading information presented
confidently as accurate.
For example:
- An AI legal tool cites non-existent case law.
- A financial AI model produces flawed projections due to incorrect training assumptions.
- A chatbot gives incorrect regulatory or medical advice.
- An automated system flags the wrong compliance breach.
- A deepfake communication triggers fraudulent fund transfer.
Unlike traditional software bugs, hallucination is not always predictable or reproducible. It arises from probabilistic modelling, pattern recognition and incomplete training data. And this makes accountability complex.
Why This Is Not Just a Technology Issue
In India, AI adoption is accelerating across Financial services, Healthcare, Manufacturing, Technology companies,
Startups, Professional advisory firms and Large infrastructure businesses
If an AI-generated output is materially incorrect and relied upon, the consequences could include:
Professional negligence claims
Contractual disputes
Regulatory penalties
Shareholder action
Reputational
damage
Financial loss due to operational decisions
And hence the key legal question arises:
Who is responsible when AI makes a mistake?
Is it the company deploying it?
The software vendor?
The board that approved the rollout?
The employee who relied on it?
Insurance policies were not originally drafted to address this ambiguity. That is where risk gaps may emerge.
Where Does Insurance Stand Today?
Most existing commercial insurance policies were built for traditional exposures. Lets review few of the relevant ones like cyber insurance, professional indemnity, directors and officers liability insurance policies.
Cyber Insurance
Cyber policies typically respond to:
- Data breaches
- Ransomware attacks
- Network interruption
- Incident response costs
They may not automatically respond to:
- Incorrect AI-generated advice
- Business losses caused by flawed algorithmic decisions
- Third-party reliance on inaccurate AI output
Unless specifically structured, coverage may be uncertain.
Professional Indemnity (PI) / Errors & Omissions (E&O)
These policies cover negligent professional services.
However, AI introduces grey areas:
- Was the error a “professional act”?
- Was there sufficient human oversight?
- Is reliance on AI considered reasonable?
If advice is primarily AI-generated, insurers may question how negligence is defined.
Directors & Officers Liability (D&O)
If AI adoption is not properly governed and consequently results in regulatory scrutiny, shareholder losses, disclosure failures, board-level liability may arise.
AI governance is increasingly becoming a board responsibility.
The Underwriting Reality
Globally, insurers are beginning to react.
Some trends emerging internationally include:
- AI-related exclusions in technology policies
- Expanded underwriting questionnaires
- Sub-limits for technology-driven errors
- Increased focus on governance and controls
While the Indian market may still be evolving, it is only a matter of time before similar underwriting discipline becomes standard.
The regulator in India, Insurance Regulatory and Development Authority of India, has already shown increasing attention toward cyber risk governance and risk management frameworks across the industry. As AI exposure grows, regulatory expectations around disclosure and controls are likely to strengthen.
What Should Indian Corporates Be Doing Now?
AI adoption should be accompanied by risk planning; not after deployment, but alongside it.
Practical steps that they can focus on are
- Establishing formal AI governance frameworks
- Defining accountability structures
- Ensuring human oversight for critical outputs
- Documenting validation processes
- Conducting vendor due diligence
- Maintaining audit trails
- Reviewing insurance coverage proactively
Insurance should be reviewed in light of AI exposure and not assumed to be adequate.
The Bigger Question: Will We See AI Liability Insurance, soon?
Possibly. Over the next 5–10 years, we may see AI-specific endorsements, algorithm liability extensions, governance warranties, structured coverage for automated decision systems. However; until the market matures, corporates must navigate the evolving landscape carefully
A Practical Perspective
AI hallucination is not about machines ‘imagining things’; it is about organisations placing reliance on systems that can produce confident but flawed outputs.
The risk is not that AI makes mistakes.
The risk is that companies fail to understand how those mistakes translate into legal liability, financial exposure, regulatory scrutiny, reputational damage and so on.
As AI becomes embedded in business operations, insurance discussions must evolve accordingly.
Boards, CFOs, and risk managers would do well to ask one simple question: if our AI system is wrong tomorrow, who pays for it or who is accountable for it?
That conversation should happen before a claim arises.
At Biswas, we believe the role of a corporate insurance broker is not just to place policies, but to anticipate emerging exposures and structure risk intelligently.
AI liability is not a future issue.
It is a present conversation.