Case Study: Fortune 500 Insurance Company achieves rapid fraud detection and faster model deployment with Domino Data Lab

Delivering new fraud detection capabilities in weeks, not months

A Fortune 500 insurance leader with 7 distributed data science groups and 170 users was struggling to scale fraud detection: centralized R/Python servers were oversubscribed, many data scientists resorted to working on laptops (creating shadow IT), and teams lost time recreating cleaned data, models, and project history. Manual handoffs, lack of reproducibility and audit trails, and lengthy recoding and validation cycles meant new models could take months to reach production and governance was hard to enforce.

By adopting the Domino platform and integrating tools like DataRobot, H2O, Python, R, SAS and Trifacta, the company created a self-service, reproducible environment with containerized compute, API/web app deployment and CI/CD integration. This accelerated development and deployment—enabling a more sophisticated fraud model to go live in weeks, not months—recouped an estimated 10% of staff time, trimmed months off model deployment and validation cycles, and delivered stronger governance and auditable reproducibility.