Insurance. Six workloads, one endpoint.
Six insurance AI workloads — AI underwriting and risk pricing, claims adjudication, fraud network analytics and policy document intelligence among them — run on one OriginChain deployment across life, general, health and takaful carriers. Each needs more than one data model at once, which is exactly why they stall on a stitched stack.
Life, general and health insurers, takaful operators, TPAs and large broking groups across India and the Gulf, modernising policy administration alongside an AI mandate.
The forcing function.
The IRDAI Fraud Monitoring Framework demands enterprise-wide, cross-functional fraud oversight — structurally impossible when claims, underwriting and distribution data sit in separate systems. Detecting coordinated schemes needs graph traversal and pattern search across policies and networks simultaneously; isolated rule engines cannot see the ring.
The claims ecosystem graph — coordinated rings run through agents, hospitals and garages; per-claim scoring structurally cannot see them.
health-claims fraud is detectable only across policies and networks, never inside a single claim
and abuse are a standing cost line in health insurance — visible only when claims, providers and policies are read together
AI working group shaping audit-ready, explainable-AI standards for claims and fraud
Six workloads, five query shapes.
All six workloads need SQL, four need vector search, three need full-text, and two each need graph traversal and /ask — which is why single-model databases deliver them only with a second system and a sync problem.
Score risk against decades of historical loss experience via vector similarity, joined live with policy, telematics and wearable data for instant, evidence-backed pricing.
Faster quote-to-bind; better loss-ratio discrimination at the same acceptance rate.
Straight-through processing for high-volume claims — full-text search over documents and medical bills, conversational triage, and human-in-the-loop control on the exceptions that matter.
Higher STP rate and lower cost per claim, with turnaround as a retention lever.
Model the claims ecosystem as a live graph — agents, hospitals, garages, policyholders and repairers — to surface coordinated rings invisible to any single-entity rule engine.
Ring-level recoveries that per-claim scoring structurally cannot find.
Multilingual retrieval for agents, advisors and customers — hybrid Arabic-and-English search across wordings, endorsements and product catalogues.
Fewer mis-quoted terms; sharply reduced advisor dependence on product helpdesks.
Every policy, interaction and life event in one queryable view; graph relationships plus semantic search drive next-best-action and churn prevention at renewal.
Renewal-rate improvement and higher policies-per-household.
Every automated decision logged to a single source of truth — audit-ready trails for IRDAI, DPDP, SAMA, IFRS 17 and DIFC, answerable in plain language.
Explainability as a standing property of the platform rather than a per-model project.
Impact statements are directional targets referenced to published industry research — calibrate against your own baseline.
The stack this replaces in Insurance.
Five licences, five sync jobs, five security perimeters — collapsed into one atomic store.
Underwriting, claims and fraud reasoning over one graph — which is what ‘enterprise-wide fraud oversight’ actually requires in practice.