The Case for AI in Insurance

# The Case for AI in Insurance

Insurance is not the sexiest industry for ML engineers. But if you want to build systems with immediate, measurable impact — fraud costs the global insurance industry over $80B annually — this is where the stakes are real.

The Fraud Detection Challenge

When I joined AXA Mansard, the fraud investigation team was working with a 3-hour average response time. The existing system was rule-based and completely blind to novel fraud patterns.

Building Real-Time Detection

We rebuilt the pipeline around event streaming: Claims Event → Kafka Topic → PySpark Streaming → ML Scoring → AWS Lambda → Alert.

Using an ensemble of gradient boosting, autoencoders, and graph-based features, we reduced average response time from 3 hours to 5 minutes.

Results

Cross-sell recommendation engine: 30% improvement. Fraud detection: response time from 3 hours to 5 minutes. Recommendation engine: 45%+ increase in transactions.