1.1 Capabilities Offered by Compliance Studio

Compliance Studio has inbuilt advanced analytics for fighting financial crime on a robust platform that also allows for management and governance of user defined models and close integration with the Oracle Financial Services Crime and Compliance Management suite of applications.

  • Specific Use Cases for Financial Crime
    • Behavioral ML models and Custom rules-based scenario frameworks for identifying suspicious patterns of behaviour and generating alerts for review
    • Sanctions and AML Event Scoring for false positive prediction and disposition
    • Automated Scenario Calibration and Scenario Conversion Utility for Oracle AML Scenarios
    • Customer Risk Scoring
    • Customer Segmentation and Anomaly Detection
    • Typology Detection
  • The following specific use cases are supported by the ML Foundation for Financial Crimes
    • Integrated with Oracle Financial Crime Application Data and readily usable across the enterprise financial crime data lake
    • Pre-engineered features and transformations to address each use case
    • Simplified APIs for each stage of the modeling lifecycle
    • Leverage the power of Graph, Supervised ML, and Unsupervised ML to build typology detection models, detect anomalies, and risk score customers or events
    • Ongoing Monitoring of Model Performance and Concept Drift
  • Entity Resolution for Detection and Investigations
    • Entity Resolution to enhance monitoring effectiveness and provide a single customer view
    • Linking and Resolution across internal and external data to improve single entity detection and enhance investigations
    • Allows for Scenario/Model detection across internal data
    • Multi-attribute enabled with ML boosts for Name/Address models
    • Prebuilt Integrations and easily configurable for Data Sources like ICIJ, Safari, etc
  • Graphs
    • Graph Pipeline feature allows you to view the data relationships in a graphical format.
    • Graph Analytics will give Financial Institutions the ability to monitor the data financial institutions effectively. The data is organized as nodes, relationships, and properties (property data is stored on the nodes or relationships). The results of analytics algorithms are stored as transient properties of nodes and edges in the Graph.
  • Model Management and Governance
    • End-to-end management from model creation to model deployment
      • Data Ingestion (Oracle DB, Graph, Hive)
      • Model Development
      • Supports virtually all open source packages, interpreters, etc.
      • Process in Database or Big Data
      • Model Training
      • Model Performance Evaluation
      • Model Explainability
      • Model Tracking and Audit
      • Approval Mechanisms
      • Model Deployment
      • Scheduling
      • Ongoing Monitoring
      • Analytics of Choice
      • Choose from our proprietary models or bring your own
      • Fully embedded Graph Analytics Engine and Financial Crime Model
      • Embedded with a highly scalable in-memory Graph Analytics Engine (PGX)
      • Industry's most intuitive Graph Query Language to gain rapid insights
  • Analytics of Choice
    • Choose from our proprietary models or bring your own
    • Fully embedded Graph Analytics Engine and Financial Crime Model
    • Embedded with a highly scalable in-memory Graph Analytics Engine (PGX)
    • Industry's most intuitive Graph Query Language to gain rapid insights
  • Integrated with Oracle Financial Crime and Compliance Applications
    • Fully defined and sourced Financial Crime Graph Model supporting detection and investigation
    • Integration with ECM and Investigation Toolkit to provide meaningful guidance to investigators for rules-based and ML-generated alerts
    • Enterprise-ready and compatible with the underlying OFSAA framework
    • Works with earlier 8.0.x releases of Oracle Financial Crime and Compliance Management Anti Money Laundering (AML), Enterprise Case Management, and Fraud applications