MicrosoftLive source

Adastra’s AI-Powered Claims Risk Analysis

Use case typeFraud detectionUpdated Jun 13, 2026

Adastra enhanced fraud detection for insurers by deploying a claims risk analysis system leveraging AI and Microsoft Azure ML models. By analyzing historical data, this solution automates fraud detection and document trends, modernizing operations and reducing costs.

Organization
Adastra
Industry
Insurance
Location
Global
Published
April 2025

Reported outcomes

Strategic outcomes

Risk & complianceAutomated fraudulent claims detectionBetter decisions & insightImproved claims trend analysisRisk & complianceEnabled real-time account takeover mitigationBetter decisions & insightEnhanced forecasting and workforce planning

Primary read

Use case focus

Showing 3 of 4

  • 1Automated Insurance Claims Fraud Detection with Azure ML
  • 2Real-Time Account Takeover Risk Analysis and Alerting
  • 3Claims Trend Analysis for Operational Forecasting
  • Manual fraud detection processes led to missed fraudulent claims and high operational costs
  • Large volumes of claims data made it difficult to identify trends and outliers
  • Ineffective account takeover detection risked increased fraudulent activity
  • Limited ability to predict and prepare for claim volume spikes and seasonal trends
  • Deployed Azure ML models to automatically detect fraudulent insurance claims
  • Implemented claims trend analysis to identify causes of peaks and valleys in claims
  • Integrated graph database for advanced querying of potentially fraudulent claims
  • Enabled real-time alerting and retraining for account takeover scenarios
Technologies
  • Reduced fraudulent claim payouts, lowering operational losses
  • Improved detection speed and accuracy of fraudulent claims
  • Enabled real-time mitigation of account takeover risks
  • Enhanced forecasting and workforce planning for insurance operations
Implementation partners1
Sources & evidence1
Live sourceStill referenced

The case's original source is still reachable.

  • Cited source last checked Jun 12, 2026 — ok (0/1 broken).

Measures whether this deployment's public evidence persists — not whether the system is still in production.

Groundedness: Unavailable

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