Uncover the hidden networks behind insurance fraud, claims, and risk.
Associative AI that connects fragmented data, resolves identities, and reveals relationships traditional systems cannot detect.
- Detect fraud rings, not isolated cases
- Full network context for claims, instantly
- Resolve fragmented identities across systems
- Strengthen underwriting with connected risk insight
Insurance data is connected. Your systems are not.
The cases you can't see today are the ones costing you the most.
Fragmented data
Disconnected systems hide critical relationships across claims, policies and actors.
Hidden fraud networks
Fraud operates in networks, not single claims. Rule-based systems see only symptoms.
Broken identity
Duplicate and conflicting entities prevent accurate underwriting and investigation.
What this is costing you
Every day without a connected view of risk has measurable, compounding cost.
of organised fraud goes undetected by single-case rules
investigation time without unified network context
risk when entity data is duplicated or fragmented
in manual data stitching across siloed systems
From fragmented data to connected intelligence
One associative layer across fraud, claims and underwriting — built on your existing data.
Network-based fraud detection
Surface organised rings by linking actors, claims, providers and devices into a single graph.
Claims intelligence
Give investigators full context — connected entities, prior incidents, suspicious patterns — instantly.
Identity resolution
Resolve duplicate, partial and conflicting entities into trusted, persistent identities.
Underwriting insight
Price and accept risk with the relationships and exposure your current systems can't see.
Built for real insurance complexity
Fraud ring detection
Detect organised rings across claims, policies and actors.
Claims clustering
Cluster related claims to expose coordinated patterns.
Identity matching
Resolve people, businesses, vehicles and assets at scale.
Provider pattern analysis
Spot anomalous repair shops, clinics and intermediaries.
Risk network analysis
Visualise exposure across connected entities and portfolios.
From raw data to decisions in five steps
Connect data
Ingest claims, policies, parties, devices and external signals.
Resolve identities
Merge duplicates into trusted, persistent entities.
Map relationships
Build the associative graph across actors and events.
Generate intelligence
Score risk, surface rings, explain every connection.
Drive decisions
Push insight into investigation, claims and underwriting workflows.
Move from records to relationships
Designed for real insurance complexity
Engineered to operate across the full breadth of enterprise data, actors and workflows.
- Works across claims, policies and actors
- Handles large enterprise datasets
- Supports investigation workflows end-to-end
- Integrates with claims, fraud and core systems
Enterprise-ready by design
Explainable AI
Every signal traceable to the entities and edges that produced it.
Integration ready
Connect to core, claims, fraud and data platforms with first-class APIs.
Secure architecture
Enterprise-grade controls, encryption in transit and at rest.
Auditability
Full lineage and decision history for compliance and review.
Human-in-the-loop
Investigators stay in control, with AI as decision support.
Frequently asked questions
See what your current systems are missing
A 30-minute demo on your context. No fluff — just the connections changing decisions.
Talk to an associative AI specialist
Tell us about your context. We'll tailor the demo to your fraud, claims or underwriting priorities.
- 30-minute working session
- Tailored to your data and workflows
- No procurement friction