AI Insurance Claims Processing

Claims processing averaged 11 days. More than half of claims required manual review because documentation was inconsistent. Customer satisfaction was slipping, and operating costs per claim averaged $118. The carrier needed faster turnaround without adding adjuster headcount.

185K historical claims records, 72K supporting documents (photos, police reports, repair estimates), 14K adjuster notes, and weather/event correlation data. The data covered auto and property lines of business across 12 Midwest and Southeast states.

• Vision Transformer: Fine-tuned for damage assessment from claim photos—severity estimation, damage type classification, and repair cost prediction.
• Fine-tuned BERT: Claims narrative classification—routing claims to the right department based on loss description text.
• XGBoost: Real-time fraud scoring using 200+ features including claim patterns, provider history, and timing anomalies.
• GPT-4: Adjuster recommendation generation—summarizing claim details and suggesting next steps for complex files.

Event-driven microservices: claim intake → document classification → entity extraction → damage assessment → fraud screening → routing engine → adjuster dashboard. Real-time scoring with sub-second latency for straight-through processing decisions.

Apache Kafka for event streaming ensures no claim is lost in transit. Each stage publishes results to the next stage's topic, with dead-letter topics for failures requiring manual intervention.

AWS: EKS for container orchestration, PostgreSQL for claims data, managed vision APIs for damage assessment, Kafka for event streaming, Grafana + Prometheus for monitoring. Blue-green deployments for zero-downtime model updates.

Multi-modal data alignment was the core challenge—combining text narratives, damage photos, structured claim records, and temporal patterns into a unified scoring model. Fraud detection needed to balance sensitivity vs. false positives: wrongly flagging legitimate claims destroys customer trust faster than missing occasional fraud.

State-by-state regulatory variations required a configurable rule engine—claims handling timelines, required disclosures, and settlement procedures differ by jurisdiction.

Straight-through processing should start with the simplest, most predictable claim types and expand gradually. We launched with simple windshield claims (high volume, low complexity), proved the system's accuracy, then expanded to collision and property damage.

The hardest part isn't the AI—it's change management with adjusters who fear automation. Involving adjusters in the design process and positioning AI as "handling the boring claims so you can focus on complex ones" was critical for adoption.