Portfolio Accelerator · Insurance · Microsoft Azure
Claims Fraud Detection on Azure: An Implementation Blueprint, Not a Slide Deck
A documented Azure Synapse architecture for claims fraud detection, combining anomaly scoring, graph link analysis, and explainable adjudication.
problem
Why Insurers Are Moving Claims Fraud Detection to Azure Synapse
Rules-based Special Investigations Unit workflows catch the fraud patterns someone already thought to write a rule for. They miss coordinated rings, and they generate enough false positives that investigators triage by gut feel as much as by score. An Azure-native insurer wants a fraud architecture that lives on the same Synapse platform already running its claims data, not a bolt-on point solution.
demo
Inside the Kriv AI Claims Intelligence Architecture on Azure
This page shows the architecture for Kriv AI's claims intelligence accelerator on Azure. In the interest of transparency: the environment setup and reference-architecture review are in progress; the data warehouse, fraud model, and AI agents are specified but not yet built. We're presenting this honestly as a blueprint, not a finished demo.
Data Layer: Synapse Pipelines and Synthetic Claims Data
The design calls for a medallion-style Synapse data warehouse (bronze, silver, gold layers) built on synthetic claims data drawn from realistic public reference sources, an auto-insurance fraud dataset, the Porto Seguro driver dataset, and FEMA flood claims data, so the architecture can be demonstrated without any real policyholder data.
Model Layer: Anomaly Detection, Graph Link Analysis, and Governed AI Agents
The plan combines a statistical anomaly-scoring model (targeting an AUC-ROC above 0.90) with graph link analysis across claimants, providers, and addresses to surface collusion patterns a per-claim model alone would miss, plus five named AI agents on Azure AI Foundry, a ClaimsCoordinator, FraudAnalyst, ClaimsTriageAgent, SubrogationAdvisor, and ComplianceMonitor, each scoped to a specific role in the claims workflow.
Governance Layer: Explainability and Audit Trail for Regulators
SHAP-based explainability and a Fairlearn fairness assessment are designed into the fraud model from the start, with Microsoft Purview sensitivity labeling and a full audit trail intended to satisfy the specific NAIC compliance and SIU documentation requirements insurance regulators expect.
status
What's Real Today, and What's Roadmap
Honestly: the environment setup for this accelerator is in progress (roughly 40% complete) and 27 reference repositories have been reviewed across Synapse, Azure ML, Document Intelligence, Purview, and explainability tooling. The Synapse data warehouse, fraud detection model, AI agents, and Power BI dashboards described above are designed in detail but not yet built and exercised on synthetic data. This same underlying platform also backs our fraud ring network analysis accelerator, and both pages will be updated with real, measured results once the pipeline is running end to end.
differentiation
Why an Implementation Partner Beats a DIY Build or a Big 4 Assessment
A Big 4 claims fraud modernization engagement typically opens with a current-state assessment and a staffing plan. Kriv AI starts from a fully specified Azure architecture, five named AI agent roles, and a governance layer already mapped to NAIC expectations, shortening the distance from advisory conversation to a working pilot.
engagement
Fit and the 90-Day Path to Production
A scoped engagement builds this architecture out against your real Synapse workspace and claims data, carrying the same explainability, fairness testing, and audit-trail design through from day one.
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Straight answers
Frequently asked questions about Claims Fraud Detection on Azure: An Implementation Blueprint, Not a Slide Deck
Is the claims fraud detection accelerator built and running on Azure today?
No, and we want to be direct about it. The environment setup is in progress (roughly 40% complete) and the architecture is fully specified, but the Synapse data warehouse, fraud model, and AI agents are not yet built and exercised on synthetic data.
What Azure services does the architecture use?
Azure Synapse Analytics for the claims data warehouse, Azure ML for the fraud detection model, Azure AI Foundry for the five named claims agents, Document Intelligence for claim document extraction, Microsoft Purview for governance, and Power BI for dashboards.
Is any real claims data used?
No. This accelerator, once built, will run entirely on synthetic claims data drawn from public reference datasets. No real policyholder data is used at any stage.
How does graph link analysis help catch fraud that per-claim scoring misses?
It links claimants, providers, and addresses across many claims to surface coordinated fraud rings, the same network-analysis capability described on our fraud ring network analysis accelerator page, which shares this platform.
How does this map to NAIC compliance expectations?
SHAP explainability, a Fairlearn fairness assessment, and a Microsoft Purview audit trail are designed into the model from the start, the specific documentation an SIU and compliance team need for regulatory review.
Can Kriv AI build this out for our carrier's Azure environment?
Yes. A scoped engagement can build this architecture against your real Synapse workspace and claims data. Contact us to discuss scope and timeline.
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.
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