Infographic
Understanding Entity-Level Identity Resolution
Connecting fragmented data across devices, channels and systems to identify a single customer as interactions happen — walked through on one real insurance household.
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Worked household example
4 industries
Free to share
The Problem: Fragmented Identities
One household, three systems, no connection
Insurance providers see father, mother and son as three unconnected profiles — each living in a separate system, with no awareness of their shared household, behaviour or combined risk.
Father
Auto policy — System A
Mother
Home policy — System B
Coverage gaps go undetected. Cross-sell opportunities are missed. Fraud signals remain invisible.
One Signal Changes Everything
The father opens the app to check his auto policy
1
Login event
The father opens the mobile app.
2
Signal detected
Device, IP and behaviour are captured.
3
Resolution begins
The engine scans for linked identities and household connections.
The engine connects all family members into a single household identity graph — in milliseconds.
The Outcome: A Unified Household View
Gaps, risks and opportunities in one profile
Missing health insurance
The son has no health coverage — flagged as a high-priority gap across the household.
Coverage gaps detected
The mother's home policy lacks flood and contents cover — surfaced automatically.
Cross-sell opportunities
Bundle offers triggered in real time: life, health and multi-vehicle discounts.
Business Impact
Four things that change downstream
Faster claims
Resolve instantly, with full context.
Fraud detection
Spot linked fraudulent signals.
Personalized offers
Trigger relevant cross-sells.
360° customer view
Unified, real-time entity-level profiles.
The same logic applies across banking and finance, retail and e-commerce, healthcare, and travel and hospitality.
From a Single Interaction to a Complete Household Graph
Turn fragmented signals into a unified, real-time entity-level customer view — in milliseconds, across every industry.
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