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.