Knowledge Graphs for Insurance

Knowledge graphs are reshaping insurance from the inside out — turning siloed policy, claims, and third-party data into a unified, traversable network of entities and relationships. In an industry built on the accurate assessment of risk and the detection of deception, the ability to reason across interconnected webs of people, properties, events, and organizations is not a technical nicety — it is a competitive and actuarial imperative. By early 2026, knowledge graphs have moved from experimental pilots into core underwriting, fraud, and claims infrastructure at leading carriers worldwide.

Fraud Detection and Fraud Ring Intelligence

Insurance fraud costs the U.S. property and casualty industry an estimated $80–$100 billion annually, and traditional rule-based detection systems are poorly equipped to catch the most damaging schemes: organized fraud rings. A ring might involve a staged auto accident with ten participants — claimants, a compliant physician, a specific attorney, and a body shop — each appearing individually legitimate. Knowledge graphs expose the ring by modeling every entity and relationship: shared phone numbers, overlapping IP addresses, common legal representatives, recurring repair facilities, and geographic clustering of incidents. When a new claim arrives, graph traversal scores it against hundreds of latent connection paths, not just direct attributes.

Shift Technology, whose AI fraud detection platform is deployed at more than 100 insurers globally including AXA and Generali, uses graph-based entity resolution to surface hidden networks within incoming claims data. Similarly, Quantexa's Decision Intelligence platform — adopted by insurers including Zurich Insurance Group — builds dynamic entity networks that link internal claims records with external data sources (corporate registries, court records, address databases) to detect synthetic identities and provider fraud in real time.

Underwriting and Risk Graph Modeling

Underwriting is fundamentally a problem of relational context: the risk of insuring a commercial property is not captured by its square footage alone, but by its proximity to flood zones, the claims history of neighboring properties, the litigation propensity of its jurisdiction, and the credit behavior of the entity that owns it. Knowledge graphs allow underwriters and automated pricing engines to traverse these contextual relationships at query time rather than pre-joining them in static tables.

Moody's RMS, the catastrophe modeling firm that anchors global reinsurance pricing, has integrated graph-based exposure representations into its risk intelligence products, allowing cedents and reinsurers to model correlated losses across portfolios of interconnected risks. On the commercial lines side, insurers including Chubb and Hartford are piloting GraphRAG-enabled underwriting assistants that traverse policy precedent graphs, hazard databases, and loss run histories to generate structured risk narratives for complex accounts — dramatically compressing the time needed to quote mid-market and E&S risks.

Claims Intelligence and Automated Adjudication

Modern claims management requires reasoning across a heterogeneous set of documents, structured records, and temporal sequences: the First Notice of Loss, the adjuster's inspection notes, the repair estimate, the medical billing codes, the policy endorsements, and the claimant's prior loss history. Knowledge graphs provide a unified semantic layer over this heterogeneity, enabling AI agents to traverse from a claim node to related policy terms, to comparable settled claims, to medical procedure cost benchmarks — without requiring a human adjuster to manually cross-reference disparate systems.

Guidewire Software, which provides core systems to over 500 insurers including Farmers Insurance and IAG Australia, has deepened its integration with graph-based data architectures as part of its Guidewire Cloud Platform evolution, enabling insurers to build connected claims intelligence on top of their existing policy and billing data. FRISS, a specialist AI platform for claims and underwriting fraud, uses real-time graph scoring that evaluates every claim against a continuously updated network of known fraud indicators at the moment of first notice.

Customer Intelligence and Portfolio Management

Insurance carriers manage customers across product lines — auto, home, umbrella, life — often in disconnected systems that make a unified view nearly impossible. Knowledge graphs provide the semantic bridge: a single customer node connects to all associated policies, household members, vehicles, properties, beneficiaries, agents, and interaction histories. This Customer 360 view enables cross-sell identification, churn prediction, and personalized service at a level of fidelity that traditional CRM systems cannot approach.

LexisNexis Risk Solutions, which supplies data and analytics to over 98% of U.S. auto insurers, uses large-scale entity resolution graphs — linking driver records, vehicle histories, address changes, and telematics profiles — to provide insurers with enriched risk scores at the point of quote. Their graph infrastructure resolves hundreds of millions of identity records into coherent entity networks that inform both pricing and fraud triage simultaneously.

Regulatory Compliance and Solvency Mapping

The regulatory environment for insurance is among the most complex of any sector — NAIC model laws in the U.S., Solvency II and IFRS 17 in Europe, and a patchwork of state-level requirements govern everything from rate filings to capital reserves. Knowledge graphs model regulatory obligations as structured nodes linked to the specific business processes, data assets, and reporting entities they govern, enabling compliance teams to perform impact analysis when regulations change and to trace data lineage from source systems to regulatory reports. This capability has become especially valuable for large multinationals navigating divergent requirements across dozens of jurisdictions simultaneously.