DataVisor UML Modeling Studio (dCube) is a machine learning model development and deployment platform designed for fraud detection and financial crime prevention. It enables organizations to build, validate, and deploy fraud detection models by combining domain expertise with unsupervised machine learning (UML) techniques. The platform covers the full model lifecycle across four core capability areas: Data Integration and Transformation: Users can upload and map data fields, after which the platform automatically analyzes data quality and flags potential issues to accelerate data cleaning and preparation. Advanced Feature Engineering: The platform provides a library of over 1,000 pre-built fraud detection features derived from attributes such as device IDs, user agents, and email addresses. These features are tailored to specific fraud use cases to enhance detection accuracy. Model Development and Validation: Users can develop, compare, and refine machine learning models through a user-facing modeling interface. The platform supports model governance with transparent processes, explainable results, and complete documentation. It allows users to incorporate DataVisor's domain expertise alongside their own configurations. Case Management and Knowledge Graph: The platform includes case management tools to support fraud investigation workflows. It enables analysts to investigate complex cases, uncover patterns, and manage team review processes efficiently — claiming a 100x improvement in review efficiency. Deployment options include public cloud, private cloud, and on-premise environments. The platform targets financial institutions including banks, credit unions, fintechs, and digital payment providers across use cases such as ATO prevention, card fraud, check fraud, ACH/wire fraud, application fraud, and AML compliance.