29,000+ ML Models for Multinational Insurer
The Pain
A large multinational insurance company headquartered in Brussels needed to scale their AI capabilities across fraud detection, claims processing, and underwriting. Their data science teams were spending excessive time on infrastructure and model deployment rather than focusing on business outcomes. They needed a unified platform that could handle the entire ML lifecycle [citation:6].
The Intervention
I implemented an enterprise AI platform that unified model development, deployment, and monitoring. The solution enabled data scientists to rapidly experiment with 1,438 projects and build 29,410 ML models. I established automated workflows for model explainability checks, risk management compliance, and production deployment pipelines [citation:6].
The Profit
The client now runs 4,436 models in production, generating 524 million predictions monthly. The platform has delivered massive value across fraud detection (catching more fraudulent claims), claims processing (automating routine decisions), and underwriting (more accurate risk assessment). Data science productivity increased dramatically as teams focused on modeling rather than infrastructure [citation:6].