Surya Sai Turaga

Ensemble Health Partners manages revenue cycle operations for 30+ health systems, processing 25 billion+ transactions across 800TB of healthcare financial data. Legacy SQL Server sprawl —1 5 individual databases with separate schemas —c reated silos that blocked cross-client insights and slowed AI innovation in an industry where providers operate on 1% margins. This session shows how Ensemble consolidated onto Databricks Lakebase as their AI-ready transactional layer, eliminating brittle pipelines and enabling self-service pods to materialize views directly from harmonized Delta tables. The results: 3–5% net revenue yield improvement ($30M+ per $1B health system), 20% organizational efficiency gain, and 10x reduction in failure rates — while powering agentic AI workflows for denial prevention, AR follow-up, and payer-behavior prediction at scale.

Many legacy SIEMs charge on ingestion, lock data behind proprietary formats, and limit AI customization. Databricks Lakewatch changes that. As the Open Security Lakehouse, Lakewatch lands 100% of your telemetry in open formats on Delta Lake — and because the data is open, security teams can pull any signal into custom apps built natively on Databricks. This session walks through a production SecOps platform on Lakewatch, ingesting 13TB/day across 22 sources at up to 80% lower cost. OCSF-normalized SDP pipelines deliver sub-15-minute detection with 94% MITRE ATT&CK coverage. A React + FastAPI app on Databricks Apps, backed by Lakebase, shows the art of the possible — AI threat hunting with Claude and custom analyst workflows no prebuilt SIEM could surface.

Get the most out of your investments with Azure Databricks. Learn how the deep integration in the Azure ecosystem accelerates workloads, optimizes performance, and maximizes value.

We will present a framework for FinCrime detection leveraging Databricks lakehouse architecture specifically how institutions can achieve both data flexibility & ACID transaction guarantees essential for FinCrime monitoring. The framework incorporates advanced ML models for anomaly detection, pattern recognition, and predictive analytics, while maintaining clear data lineage & audit trails required by regulatory bodies. We will also discuss some specific improvements in reduction of false positives, improvement in detection speed, and faster regulatory reporting, delve deep into how the architecture addresses specific FATF recommendations, Basel III risk management requirements, and BSA compliance obligations, particularly in transaction monitoring and SAR. The ability to handle structured and unstructured data while maintaining data quality and governance makes it particularly valuable for large financial institutions dealing with complex, multi-jurisdictional compliance requirements.