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"Why was this claim rejected?"
Your AI must have an answer.
Claims automation, underwriting AI, pricing models, risk flags, insurance runs AI at the exact decision points where regulators and courts demand explainability. A black-box model cannot answer the only question that matters.
Regulators worldwide have raised the bar, and they're specific
Explainability on demand
Claims denials, pricing decisions, and risk flags face direct explainability demands. When the policyholder, the ombudsman, IRDAI, or an EU supervisory authority asks why, "the model decided" is not an acceptable answer. The EU AI Act classifies insurance underwriting and claims assessment as high-risk under Annex III.
IRDAI · EU AI Act (Annex III) · NAIC
Records, in India, structured
The 2025 Information & Records Regulations require India-only record storage and a structured data governance framework. Your AI decision records are records.
IRDAI 2025 regulations
CISO independence & cadence
Revised information and cyber-security guidelines mandate CISO independence from IT, quarterly risk-committee meetings, CERT-In alignment, and DPDP alignment. In the EU, DORA and Solvency II impose digital operational resilience requirements on insurers. In the US, NYDFS cybersecurity regulations set comparable standards.
IRDAI cyber guidelines · DORA · Solvency II · NYDFS
Cyber at the top of the CRO stack
80% of insurance CROs rank cyber among their top-five risks. AI agents touching claims and customer data sit squarely inside that exposure. The NAIC model bulletin on AI and the growing number of US state-level insurance AI regulations add compliance obligations that vary by jurisdiction.
EY/IIF Insurance CRO Survey 2026 · NAIC AI bulletin · State AI laws
How Nexovern answers
A complete inventory of every model and agent touching claims, underwriting, and pricing (MAP). Action-level evidence behind each automated decision, what data was read, what process produced the outcome, retained under your records framework (MEASURE). Enforcement gates on high-impact decisions, with human checkpoints where your policy requires them (MANAGE).
MAP · MEASURE · MANAGE
Map, Measure, Manage, for decisions policyholders can challenge
Map
A complete inventory of every model and agent touching claims, underwriting, pricing, and distribution, classified by decision impact and mapped to accountable owners.
Measure
Action-level evidence behind each automated outcome: what data was read, what process produced the decision, what rules applied, retained under your records framework, in-country where required.
Manage
Human checkpoints on denials and high-impact decisions, data boundaries aligned to IRDAI, DPDP, GDPR, and NYDFS obligations, and enforcement logs that prove the checkpoints operate, meeting DORA operational resilience requirements.
- For the policyholder / ombudsman: "Why was this claim rejected?", the reconstructed decision path, in plain terms.
- For IRDAI: "Show us your AI decision records and data governance.", structured records, stored under your residency requirements.
- For EU AI Act / DORA compliance: "Show your high-risk AI documentation and operational resilience controls.", mapped to Annex III and DORA Chapter II.
- For the NAIC / state regulator: "How do you govern AI in underwriting and claims?", evidence of bias testing, human oversight, and the controls the NAIC model bulletin expects.
- For the risk committee: "Which automated decisions ran without the required human checkpoint?", by design, none; here is the enforcement log.
- For the DPDP / GDPR audit: "What personal data did your AI systems access?", access records tied to the agent, queryable inside the 72-hour clock.
Make every automated decision defensible.
We're engaging with insurance enterprises across India, the US, and Europe. A demo maps your claims and underwriting AI against IRDAI, DPDP, EU AI Act, DORA, NAIC, and ISO 42001 expectations.