Portfolio Accelerator · Insurance · Network-Graph Fraud Detection
Fraud Ring Network Analysis AI: A Working Accelerator, Not a Slide Deck
Graph-based fraud ring detection built on Kriv AI's Azure Claims Intelligence & Fraud Detection Platform — real cloud infrastructure, synthetic claims data.
problem
Why Per-Claim Fraud Scoring Misses Organized Fraud Rings
A per-claim fraud score catches an individual suspicious claim. It does not catch a ring of claimants, providers, and repair shops that individually look clean but collectively file dozens of claims against each other's addresses, phone numbers, and bank accounts. Detecting that pattern requires a network-graph view across claims, not just a per-claim model.
demo
Inside the Accelerator: Network-Graph Fraud Detection on Azure
This page showcases the network-analysis layer of Kriv AI's Azure Claims Intelligence & Fraud Detection Platform, one of our most complete portfolio accelerators — built on real Azure infrastructure against a large synthetic claims dataset spanning four lines of business.
Data Scale and Architecture
The platform runs approximately 3.7 million synthetic records across four lines of business — auto, property, health, and flood — sourced from named public reference datasets (a Kaggle auto-insurance fraud dataset, the Porto Seguro driver dataset at roughly 595,000 records, FEMA's National Flood Insurance Program dataset at over 2 million records, Synthea-generated synthetic patients, and Census ACS data for demographic context), processed through an Azure Synapse medallion lakehouse.
On top of the per-claim layer, a network-graph model links claimants, providers, addresses, and payment details to surface ring-shaped fraud structures that a single-claim model can't see — the specific gap this page targets.
Governed AI Agents and Fairness Controls
Five AI agents run on Azure AI Foundry — ClaimsCoordinator, FraudAnalyst, ClaimsTriageAgent, SubrogationAdvisor, and ComplianceMonitor — each scoped to its own role, with a hard rule that no agent auto-approves a payout above $50,000 without human sign-off. Model fairness is checked with Fairlearn alongside XGBoost and SHAP explainability, and the full claims-to-decision trail is retained in Microsoft Purview for a 7-year audit window.
Design targets for the platform: AUC-ROC above 0.90 for fraud classification, precision at or above 0.60 when recall exceeds 0.70, a fairness disparity ratio under 1.25 across protected classes, and document-extraction accuracy at or above 95% for claims intake.
differentiation
Why Network-Graph Analysis Beats Per-Claim Scoring Alone
Most fraud-detection vendors and Big 4 analytics engagements stop at per-claim scoring. This accelerator adds the network layer on top: the same governed, explainable, audit-logged architecture as our per-claim fraud accelerator, extended with graph analysis that surfaces coordinated fraud rings a claim-by-claim review would miss entirely.
governance
Cost, Compliance, and Human Oversight
Cost-Optimized Cloud Footprint
The platform is designed to run at roughly $5 to $8 per day in an optimized configuration, or $15 to $20 per day at full capacity, and returns to $0 when torn down between demonstrations — the kind of cost discipline we bring to any production deployment we scope for a client.
engagement
From Accelerator to Production Fraud-Ring Detection
A scoped engagement adapts this network-graph layer to your claims data model and case-management system, with the same governance, fairness testing, and audit-trail architecture carried through from the accelerator.
Straight answers
Frequently asked questions about Fraud Ring Network Analysis AI: A Working Accelerator, Not a Slide Deck
How is fraud ring network analysis different from per-claim fraud scoring?
Per-claim scoring evaluates one claim in isolation. Network-graph analysis links claimants, providers, addresses, and payment details across many claims to surface coordinated fraud rings that look clean claim-by-claim but form a suspicious pattern together.
Is this tested on real insurance claims data?
No — the platform runs on approximately 3.7 million synthetic records built from named public reference datasets across auto, property, health, and flood insurance. No real client or policyholder data is used.
What AI agents are involved and what can they approve?
Five agents run on Azure AI Foundry, each with a defined role. None can auto-approve a payout above $50,000 — that always requires human sign-off, by design.
How is model fairness checked?
The platform uses Fairlearn to check for fairness disparity across protected classes, targeting a disparity ratio under 1.25, alongside SHAP explainability for every fraud score.
What does this cost to run?
The platform is designed to run at roughly $5 to $8 per day in an optimized configuration, scaling to $15 to $20 per day at full capacity, and $0 when torn down between demonstrations.
Can we see this running against a sample of our claims data shape?
Yes — we can walk through the live platform in a discovery call and discuss adapting the graph model to your claims data structure.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
Book a Discovery Call