The Challenge
This top-20 US insurance carrier processed 500,000+ claims annually across auto, property, and liability lines. Average claims cycle time was 45 days, with complex claims taking 90+ days. The Special Investigations Unit (SIU) reviewed only 3% of claims for fraud using manual referral rules, missing an estimated $50M in annual fraudulent payouts. Claims adjusters juggled 5+ systems to research a single claim. Customer satisfaction scores were declining due to slow processing times, and the carrier was losing policyholders to competitors with digital-first claims experiences.
Our Solution
Built a unified claims data lakehouse in Microsoft Fabric that consolidated data from the policy administration system, claims management platform, FNOL (First Notice of Loss) intake, third-party data providers (LexisNexis, ISO ClaimSearch), and SIU case management into a single analytical layer.
Developed a machine learning fraud detection model trained on 5 years of claims data with confirmed fraud labels. The model analyzes 60+ features including claim patterns, claimant history, provider networks, geographic risk scores, and text analysis of loss descriptions to score every claim at FNOL.
Created a claims command center dashboard in Power BI showing real-time claim volumes, cycle time metrics by adjuster/unit/region, severity distributions, reserve adequacy, and SIU referral queue with fraud probability scores.
Implemented automated workflow triggers that route high-probability fraud claims directly to SIU, escalate complex claims to senior adjusters, and flag claims requiring subrogation review — reducing manual triage by 40%.
Built adjuster workbench dashboard providing a unified view of all claim information (policy details, loss description, photos, prior claims, fraud score, similar claims) eliminating the need to switch between 5+ systems.
Results
Average claims cycle time reduced from 45 days to 22 days with automated triage and adjuster workbench.
ML model identifies 25% more fraudulent claims than the previous manual referral rules.
Additional fraudulent claims caught by the ML model saved $12M in the first year.
Automated routing rules handle initial claim triage, freeing adjusters for value-added investigation.
Every claim receives a real-time fraud probability score at First Notice of Loss.
Customer satisfaction improved 15 NPS points due to faster, more transparent claims handling.
Implementation Methodology
Phase 1 (Weeks 1-4): Data integration. Connected claims, policy, and third-party data sources into Fabric lakehouse with automated quality validation.
Phase 2 (Weeks 5-9): Fraud model development. Feature engineering, model training on historical fraud cases, validation with SIU team, and scoring pipeline deployment.
Phase 3 (Weeks 10-13): Dashboard and workflow development. Built claims command center, adjuster workbench, SIU dashboard, and automated routing rules.
Phase 4 (Weeks 14-16): Deployment and optimization. Trained adjusters and SIU investigators, tuned fraud thresholds, and activated automated workflows.