The Coverage Gap
Agents Are Settling Claims. Nobody's Explaining.
India has one of the lowest insurance penetration rates in the world — just 3.7% of GDP, against a global average of roughly 7%. Insurers are responding by deploying AI agents that autonomously underwrite policies, assess claims, and detect fraud. Motor claims agents assess vehicle damage from photographs without human review. Health claims agents validate procedures against policy terms and deny coverage in milliseconds. An estimated 30% of health insurance claims involve some element of fraud, and agents are now the first line of defence. But when an agent denies your claim, who explains why?
Our Approach
Agents Need Operations, Not Just Automation
Most insurtech vendors ship a claims model and call it done. Agentic systems — where AI reasons, decides, and acts autonomously across underwriting, claims, and fraud — need a fundamentally different governance architecture.
Agents Without Operations
- Deploy claims agent, validate accuracy on test set
- No reasoning capture — agent denies claim with no trace
- Manual fraud investigation triggered only after losses mount
- Compliance reviewed post-hoc by the audit team
- No central registry of agents or their autonomy levels
- Hope the underwriting agent doesn't discriminate by postcode or gender
Agents with Rotavision
- Every agent registered with autonomy level and risk classification
- Reasoning capture for every claim decision — full audit trail
- Multi-agent fraud detection with cross-validation across signals
- Continuous fairness monitoring for underwriting agents
- Human-in-the-loop controls for high-value and disputed claims
- IRDAI-ready audit trails from agent registry to decision output
Where It Matters
Agentic AI for India's Insurance Reality
Not generic automation — autonomous agents solving the specific problems Indian insurers and policyholders face every day.
Over 650 million Indians have no life or health insurance. For this population, there is no claims history, no policy track record, and no actuarial baseline. Traditional underwriting relies on decades of data that simply doesn't exist. Insurers are deploying autonomous underwriting agents that ingest alternative signals — hospital records, employment data, geographic risk — and price policies without human review.
But India's low penetration means these agents are serving populations the industry has never modelled before. When an underwriting agent uses pin code as a proxy for health risk, or prices women's policies higher based on maternity assumptions, the system must catch it. IRDAI expects insurers to demonstrate that AI-driven underwriting is non-discriminatory and explainable. Bounded autonomy means the agent prices within guardrails — but escalates edge cases to human underwriters.
Vishwas monitors every underwriting agent decision for fairness across Indian demographic categories, with explainability in 22 languages. Orchestrate manages the agent lifecycle — registration, policy enforcement, and reasoning capture — so insurers can extend coverage to new populations without extending risk.
Indian insurers settled over Rs 59,000 crore in claims in FY24. The volume demands automation, but claims are not a single-agent problem. A motor claims agent assesses vehicle damage from photographs — estimating repair costs and determining liability. A health claims agent validates procedures against policy terms, checking for exclusions and waiting periods. A coordinator agent routes decisions, escalates disputes, and triggers payouts.
Each of these agents reasons independently, but their decisions compound. If the motor damage agent underestimates repair costs, the settlement agent underpays. If the health claims agent misinterprets a policy exclusion, the policyholder is denied coverage they paid for. Every agent's reasoning chain must be captured, every escalation logged, and the entire multi-agent interaction auditable — because the policyholder filing a grievance has the right to know which agent made which decision, and why.
Guardian monitors each claims agent for drift and reliability in production. Orchestrate manages agent composition, policy enforcement, and human-in-the-loop approvals for high-value claims — so claims intelligence scales with volume while maintaining IRDAI-compliant audit trails.
Insurance fraud costs the Indian industry an estimated Rs 30,000–45,000 crore annually. The patterns are sophisticated and deeply networked. Hospital-insurer collusion rings where facilities bill for procedures never performed. Staged accident networks that file identical motor claims across multiple insurers. Identity fraud where the same individual holds policies under different names across companies. A single fraud detection model can't reason across this complexity.
Multi-agent fraud detection deploys specialised agents for each signal. A claims pattern agent identifies statistical anomalies across policy portfolios. A provider network agent maps relationships between hospitals, agents, and claimants. A document forensics agent validates medical records, repair estimates, and identity documents for tampering. A coordinator agent synthesises signals and assigns fraud risk scores. Each agent's reasoning is captured independently, creating an evidence chain that holds up to legal scrutiny.
Orchestrate manages multi-agent fraud workflows with cross-validation and escalation policies. Dastavez provides document AI agents that detect tampering in medical records, repair estimates, and identity documents — with every decision traceable for regulatory and legal proceedings.
Solution Package
IRDAI Agent Governance Accelerator
A combined assessment, platform, and integration package for insurers deploying AI agents across underwriting, claims, and fraud — with IRDAI inspection readiness built in.
What's Included
Audit across underwriting, claims, and fraud agent governance against IRDAI AI/ML guidelines. Gap analysis with roadmap for inspection readiness.
Orchestrate + AgentOps configured for insurance — claims agents, underwriting agents, and fraud agents registered with IRDAI risk tiers and autonomy boundaries.
Pre-built connectors for claims management systems and TPA platforms where claims agents actually operate. Governance layer alongside existing claims infrastructure.
Agent decision explanations surfaced through Bima Sugam / IGMS-compatible formats. When a claim is denied, the policyholder gets a vernacular explanation of which agent decided what and why.
Connect underwriting agent decisions back to actuarial models. Dashboard showing agent pricing behaviour vs actuarial assumptions, drift from approved pricing bands.
Platform Stack
650 million Indians have no insurance. Agents will serve them.
The question is whether those agents can explain the decisions they make.
Platform
