Case Study: a leading US-based financial services firm achieves 30% cost savings with Tiger Analytics

a leading us-based financial services firm achieves 30% savings with Tiger Analytics

Tiger Analytics partnered with a leading US-based financial services firm that sought to scale its AI capabilities. The client faced operational inefficiencies due to inconsistent deployment workflows, rising infrastructure costs, and a lack of AI observability across its portfolio of over 160 risk, fraud, and marketing models.

Tiger Analytics modernized the client's MLOps and DLOps framework using Azure ML, Databricks MLflow, and Hugging Face. This solution delivered a 30% reduction in operational costs, enabled faster deployment for more than 30 AI use cases, and provided a scalable foundation for all existing models along with real-time observability and robust governance.