Table of Contents

• Title and Copyright Information
• Document Control
• Preface
  • Audience
  • Related Resources
  • Abbreviations
  • Documentation Accessibility
  • Diversity and Inclusion
  • Conventions
  • Comments and Suggestions
• 1 About Compliance Studio Administration
  • 1.1 Capabilities offered by Compliance Studio
  • 1.2 Configurable Features
  • 1.3 Administration Overview
  • 1.4 Key Concepts
• 2 User Access and Permissioning Management
  • 2.1 Mapping User Groups
    • 2.1.1 User Groups
    • 2.1.2 User Group - Role Mapping
    • 2.1.3 Functions and Roles required to perform CRUD operations for Conda
  • 2.2 Access Compliance Studio Using SAML Realm
• 3 Interpreter Configuration and Connectivity
  • 3.1 Configure Interpreters
    • 3.1.1 python Interpreter
      • 3.1.1.1 Configure a python Interpreter
      • 3.1.1.2 Change Version in the Python Interpreter
    • 3.1.2 jdbc Interpreter
      • 3.1.2.1 Configure a jdbc Interpreter
      • 3.1.2.2 Link Wallet Credentials to jdbc Interpreter
    • 3.1.3 md Interpreter
    • 3.1.4 PGX Interpreter
    • 3.1.5 Spark Interpreter
      • 3.1.5.1 Spark Interpreter in Local Mode
      • 3.1.5.2 Enable Additional Spark Interpreter
    • 3.1.6 Pyspark Interpreter
      • 3.1.6.1 Prerequisites
      • 3.1.6.2 Configuration
      • 3.1.6.3 Use Python Virtual Environments with PySpark
      • 3.1.6.4 Configure Pyspark Interpreter
      • 3.1.6.5 Enable Additional PySpark Interpreter
  • 3.2 Create a Credential
  • 3.3 Link Credentials
  • 3.4 Create an Interpreter Group
  • 3.5 Create an Interpreter Variant
• 4 Entity Resolution
  • 4.1 Using Pre-configured Datasets and Rulesets
    • 4.1.1 Pre-configured Rulesets for Matching, Merging, and Data Survival
    • 4.1.2 Custom Rulesets for Matching
  • 4.2 FCCM out-of-the-box Entity Resolution Pipeline on FSDF
    • 4.2.1 Pre-configured Entity Resolution Pipelines
    • 4.2.2 Prerequisites for out-of-the-box ER Pipelines
      • 4.2.2.1 Creating Pre-Staging Tables in FSDF
    • 4.2.3 Load Data into Pre-Staging Tables
    • 4.2.4 Output Tables
    • 4.2.5 Entity Resolution Mapping Information
    • 4.2.6 Consolidated Information of the Resolved Entities
  • 4.3 Prerequisites when MATCHING_MECHANISM is Set to Oracle Text (OT)
  • 4.4 Executing the ER Jobs
    • 4.4.1 Create Index and Load the Data
      • 4.4.1.1 Additional Configurations
    • 4.4.2 Perform Matching
      • 4.4.2.1 Matching Output
      • 4.4.2.2 Additional Configuration for Matching with Oracle Text (OT)
    • 4.4.3 Data Survival
    • 4.4.4 Load Data in FCC_ER_OUTPUT Table
    • 4.4.5 Initial Run for High Volume Data
    • 4.4.6 Status Codes
    • 4.4.7 Using Wrapper Shell Script
  • 4.5 Persisting the Data
    • 4.5.1 Persisting the Data When F_PERSIST_GUID and F_MANUAL_APPROVAL Flags are Set to False Condition
    • 4.5.2 Persisting the Data When F_PERSIST_GUID Flag is Set to True and F_MANUAL_APPROVAL Flag is Set to True/False Condition
  • 4.6 Metadata Tables for Entity Resolution
    • 4.6.1 Default Data in the tables
    • 4.6.2 Customize Data in ER Tables
    • 4.6.3 Populate Metadata for Data Survival in the Compliance Studio Schema
  • 4.7 Removal of Entities from the Global Party (Deleted Party)
    • 4.7.1 Impact on Manual Decisioning for Deleting Parties
  • 4.8 Ability to Remove Split and Merge Action Manually
  • 4.9 Expiry of Entity Child Records Mapping
    • 4.9.1 Expiry of Entity Address Mapping
    • 4.9.2 Expiry of Entity Phone Mapping
    • 4.9.3 Expiry of Entity Email Mapping
    • 4.9.4 Expiry of Entity Document Mapping
  • 4.10 Statistics for ER Job Execution
• 5 Use Cases
  • 5.1 Automated Scenario Calibration (ASC)
    • 5.1.1 Creating ASC Workspace
    • 5.1.2 Importing Workspace Metadata
    • 5.1.3 Using Scenario Conversion Utility for ASC
      • 5.1.3.1 Conversion Steps
    • 5.1.4 Comparison of Events between BD and Conversion Utility
    • 5.1.5 Using Delete Threshold Sets Notebook
    • 5.1.6 Using Dynamic Datasets with AML Scenario Conversion
    • 5.1.7 Advanced Concepts for ASC
  • 5.2 Behavioral Model
    • 5.2.1 Creating Sandbox Workspace
    • 5.2.2 Populating Sandbox Workspace
    • 5.2.3 Importing Workspace Metadata
    • 5.2.4 Batch Framework for Behavioral Model
      • 5.2.4.1 Behavioral Model Aggregate Base Features
      • 5.2.4.2 Behavioral Model Scoring
      • 5.2.4.3 Behavioral Model Annual Model Validation
      • 5.2.4.4 Behavioral Model Monthly Model Validation
      • 5.2.4.5 Obtain the SAR Information
    • 5.2.5 Execute Batch
    • 5.2.6 Monitor Batch
  • 5.3 Sanctions Event Scoring
    • 5.3.1 Creating Production Workspace
    • 5.3.2 Prerequisites for Creating Sandbox Workspace
    • 5.3.3 Creating Sandbox Workspace
    • 5.3.4 Populating Sandbox Workspace
    • 5.3.5 Importing Workspace Metadata
    • 5.3.6 Batch Framework for Sanctions Event Scoring
    • 5.3.7 Execute Batch
    • 5.3.8 Monitor Batch
  • 5.4 AML Event Scoring
    • 5.4.1 Creating Sandbox Workspace
    • 5.4.2 Populating Sandbox Workspace
    • 5.4.3 Importing Workspace Metadata
    • 5.4.4 Batch Framework for AML Event Scoring
      • 5.4.4.1 AMLES Historic Event Load
      • 5.4.4.2 AMLES Scoring
    • 5.4.5 Execute Batch
    • 5.4.6 Monitor Batch
  • 5.5 Customer Segmentation and Anomaly Detection
    • 5.5.1 Creating Sandbox Workspace
    • 5.5.2 Populating Sandbox Workspace
    • 5.5.3 Importing Workspace Metadata
    • 5.5.4 Data Movement
    • 5.5.5 Batch Framework for Customer Segmentation and Anamoly Detection
    • 5.5.6 Execute Batch
    • 5.5.7 Monitor Batch
  • 5.6 Customer Risk Scoring
    • 5.6.1 Creating Sandbox Workspace
    • 5.6.2 Populating Sandbox Workspace
    • 5.6.3 Importing Workspace Metadata
    • 5.6.4 Obtaining SAR Labels for Customer Risk Scoring
    • 5.6.5 Obtain SAR information for Production
      • 5.6.5.1 Create a New Batch for Obtaining Investigated Entities
    • 5.6.6 Data Movement
      • 5.6.6.1 Export from Sandbox
      • 5.6.6.2 Import into Production
    • 5.6.7 Batch Framework for Customer Risk Scoring
      • 5.6.7.1 Supervised Historic Data Load
      • 5.6.7.2 Supervised Scoring
      • 5.6.7.3 Annual Model Validation
      • 5.6.7.4 Monthly Model Validation
    • 5.6.8 Execute Batch
    • 5.6.9 Monitor Batch
    • 5.6.10 ECM Connector Batch
  • 5.7 Shell Account Detection Scenario for AML
    • 5.7.1 Creating Sandbox Workspace
    • 5.7.2 Populating Sandbox Workspace
    • 5.7.3 Importing Workspace Metadata
    • 5.7.4 Batch Framework for Shell Account Detection Scenario for AML
      • 5.7.4.1 AML Scenario Processing batch
    • 5.7.5 ECM Connector Batch
  • 5.8 Custom Scenario
    • 5.8.1 Batch Framework for Custom Scenario
      • 5.8.1.1 Aggregate Base Features for Custom Scenario
      • 5.8.1.2 Event Generation for Custom Scenario
    • 5.8.2 Execute Batch
    • 5.8.3 Monitor Batch
• 6 Restart Services
  • 6.1 Stop and Start the Compliance Studio Services
  • 6.2 Stop and Start the PGX Service
• A Appendix
  • A.1 Create Metadata Indexes using Logstash
  • A.2 Unlock the Notebook
  • A.3 Checking IP Address for User’s Last Login
  • A.4 Roles, Functions and Permissions
    • A.4.1 Roles
    • A.4.2 Default Roles Seeded in Notebook Server through permissions-int.yml file
    • A.4.3 Functions in Compliance Studio
    • A.4.4 Permissions in Notebook Server
    • A.4.5 Group - Role Mapping
    • A.4.6 Role - Function Mapping
    • A.4.7 Role - Permission Mapping
  • A.5 Setting Memory of Entity Resolution and Matching Services
  • A.6 Cleanup Steps When the Create Index and Load Data Job Terminated Manually
  • A.7 Cleanup Steps When the Bulk Similarity Job Terminated Manually
  • A.8 Cleanup Steps When the Data Survival Job Terminated Manually
  • A.9 Cleanup Steps When the Load Data in FCC_ER_OUTPUT Job Terminated Manually
  • A.10 Resetting Entity Resolution Back to Day 0
    • A.10.1 Compliance Studio Schema Changes
  • A.11 Utility Scripts
    • A.11.1 Data Slicing Utility Script
  • A.12 Load Data into ICIJ Tables
  • A.13 Prescript Condition
  • A.14 Resetting Graph Pipeline Back to Day 0
  • A.15 Disable User in Compliance Studio after SSO Login
  • A.16 Migrating the Data from ElasticSearch to OpenSearch
  • A.17 Parameters for Entity Resolution Job execution
  • A.18 Conda Environment in Notebook
  • A.19 Python Libraries for Predefined Conda Environment
  • A.20 Implementation of Connection Pooling in PGX Realm
  • A.21 Configure Custom Notebook in ECM
    • A.21.1 Prerequisites
    • A.21.2 Importing Notebook
    • A.21.3 User Group Mapping
    • A.21.4 Integrating Notebook with ECM
  • A.22 How-To
    • A.22.1 How to Create Data Store
    • A.22.2 How to Register Conda Environment in BD Production Workspace
    • A.22.3 How to Create Sandbox Workspace
    • A.22.4 How to Populate the Sandbox Workspace
    • A.22.5 How to Execute Batch
    • A.22.6 How to Monitor Batch
    • A.22.7 How to Add User Defined Transformation (UDT) as Python Module
    • A.22.8 How to get Studio Alert Tables into Workspace Schema
  • A.23 Advanced Feature for ASC Use Case
  • A.24 Incremental Workspace Refresh
  • A.25 Data Model Support for AAI Applications
  • A.26 Enable Additional Spark or PySpark interpreter
    • A.26.1 Spark Interpreter User Impersonation
    • A.26.2 Sample spark-default.conf Configuration File
  • A.27 Enable Data Studio Options in Compliance Studio
  • A.28 Rebuilding Indices in OpenSearch