1.2 New Features in This Release
The following new features are incorporated in this release CS 8.1.3.2.0.
The application now has the following features and capabilities:
AML Event Scoring Enhancements
Flexible AML Event Scoring Models
Compliance Studio supports AML Event Scoring models at the scenario, entity, and segment levels. Users can configure separate models for individual scenario–segment combinations or consolidate segments into a single scenario-level model.
Business Impact: Provides greater flexibility in model development, allowing financial institutions to tailor event scoring to different customer populations while balancing model accuracy, data availability, and maintenance requirements.
Standalone AML Event Scoring
AML Event Scoring can operate independently of Behavioral Model transactional feature extraction. Users can develop scoring models using event-level data alone or enrich them with transactional features when required.
Business Impact: Simplifies model development and reduces data preparation dependencies, enabling financial institutions to implement AML Event Scoring without requiring additional transactional feature processing.
Multiple Transactional Feature Sources
AML Event Scoring supports combining transactional features from multiple Behavioral Model groups into a unified modeling dataset.
Business Impact: Enables more comprehensive event risk assessment by incorporating behavioral information across multiple customer segments and transactional datasets.
AML Event Scoring Data Management
Introduced additional data management configuration for AML Event Scoring, including supporting FCC_DM tables, database packages, and the importAMLESDMSQL.sh utility for importing metadata mappings into AMLES workspaces.
Business Impact:Supports consistent data management configuration across sandbox and production environments, improving the reliability of AML Event Scoring deployment and operation.
AML Event Scoring Scheduler Updates
Added the AMLES_Scheduler_8.1.3.2.0 scheduler configuration for AML Event Scoring workflows.
Business Impact: Supports execution of AML Event Scoring processes using the updated release-specific batch framework.
Customer Segmentation and Anomaly Detection Enhancements
Multiple Behavioral Model Data Sources
Customer Segmentation and Anomaly Detection supports combining behavioral features from multiple Behavioral Model groups into a unified modeling dataset. Source configurations are retained for subsequent production scoring.
Business Impact: Enables customer segmentation across multiple behavioral datasets while maintaining consistency between model training and production scoring.
Custom Feature Transformations
Customer Segmentation and Anomaly Detection supports configurable feature transformations that can be applied consistently during model training and production scoring.
Business Impact: Provides greater flexibility in preparing customer behavioral data while reducing inconsistencies between model development and deployment.
Enhanced Anomaly and Deviation Scoring
Customer Segmentation and Anomaly Detection provides configurable anomaly and deviation scoring, including scoring thresholds, methods, output persistence, and optional feature contribution calculations.
Business Impact: Improves flexibility in identifying customers whose behavior differs from their assigned segments or peer groups and supports more targeted investigation.
New Entity Prediction
Customer Segmentation and Anomaly Detection supports assigning previously unclustered entities to existing clusters and peer groups during scoring.
Business Impact: Enables financial institutions to incorporate new customers into established segmentation models without requiring a complete model retraining cycle.
Customer Segmentation Batch Framework
Updated the Customer Segmentation and Anomaly Detection batch framework to use Behavioral Model aggregate base features and the CSAD Scoring Batch, including support for multiple physical model groups.
Business Impact: Supports more flexible data preparation and scoring execution across different customer populations.
Model Training and Scoring Execution Controls
Introduced notebook execution tags to distinguish training activities from production scoring activities. Shared processing steps can be configured to execute in both workflows.
Business Impact: Improves consistency between model development and production execution while reducing the risk of running inappropriate processing steps.
Entity Resolution Enhancements
Real-time matching now supports Oracle Text and can create new global ids in real-time.
Business Impact: Customers using Oracle Text can use the real-time matching apis to determine if there are any existing global parties which match a new record and if not can create a new global party record without need to run the batch entity resolution process.
Graph Pipeline Enhancements
Performance Improvements
Performance Improvements have been made to the out of the box graph pipeline which now uses materialized views rather than data movement to create the graph schema. Please note the configurable user interface does not yet support this, so if the out of the box graph needs to be configured and volumes are low the existing pipeline is still recommended.
Business Impact: For processing high volumes of data for the out of the box global graph the performance times have greatly reduced.
Support for Oracle Text
The matching used to create links in the global graph now support Oracle Text, rather than needing Open Search.
Business Impact: Reduced infrastructure – Open Search clusters are no longer required.
Consolidated Health and Performance Monitoring
The legacy Environment Health tab has been replaced with a Prometheus-backed Consolidated Health Service. This introduces Current and History modes with detailed views into JVM metrics, JDBC connections, and Database Tablespaces, delivering continuous, real-time telemetry and addressing previous data accuracy gaps.
Business Impact: Establishes a single source of truth for environment health, enabling faster root-cause analysis, proactive resource management, and accurate historical trend analysis to help prevent application downtime.
Real-Time Pipeline Resource Monitoring
The Model Pipeline Execution Summary now supports optional, high-granularity live tracking of CPU utilization, memory consumption, active threads, and Process IDs (PIDs) per session. Users can manually refresh telemetry or close sessions directly from the summary view.
Business Impact: Provides immediate operational oversight to detect and terminate resource-heavy processes, prevent pipeline bottlenecks, and optimize compute costs.
Data and Pipeline Management
Cross-Tenancy Data Porting and Enhanced Discovery
- Expanded schema and table discovery — no longer limited to mapped schemas only.
- Selective data loading for granular control over what data is ported.
- Wider 75% viewport drawer with improved filtering, sorting, and clearer status messaging.
Business Impact: Reduces time and effort to prepare test environments, improves accuracy navigating complex schema lists, and accelerates development cycles.
Service Interruption Handling
Enhancement: The model pipeline now auto-tracks service disruptions. If a restart occurs during an active run, execution status automatically transitions to Interrupted — eliminating stale In-Progress states.
Business Impact: Improves operational transparency and data integrity, enabling teams to quickly identify and re-trigger interrupted processes to maintain project timelines.
Scheduling and Automation
Dynamic Scheduler Input Configuration
Enhancement: The Scheduler UI now supports dynamic, model-aware execution inputs. Parameter Sets display by default, available parameters auto-refresh on model change, and a standardized selection priority (Parameter Sets -> Optional Params -> Execution Params) ensures consistent, backward-compatible behavior.
Business Impact: Eliminates manual parameter entry, reduces configuration errors, and ensures consistent model behavior across environments with automated priority logic.
Infrastructure and Integrations
Multi-Platform Git Integration and Validation
Enhancement: The Git integration framework now supports GitHub, GitLab, and Bitbucket — removing the previous restriction to internal repositories. Includes automated repository URL validation and mandatory connection testing before any remote operations.
Business Impact: Enables teams to use preferred version control providers; proactive URL validation minimizes failed deployments and broken syncs, saving developer time.
Kafka Topic and Cluster Management
- Conda environment values can now be stored directly within topic records.
- Support for comma-separated host:port bootstrap server lists, enabling multiple cluster nodes per topic.
Business Impact: Drawer-based flows improve administrative productivity; multi-node bootstrap server support introduces high availability and fault tolerance, eliminating single points of failure in real-time data streaming.
Workspace-Aware Notification Management
Enhancement: The Data Population notification framework is now a globally managed, workspace-aware system. It automatically seeds the default mapped user group per workspace, supports dynamic recipient resolution via a searchable list, and enforces workspace-scoped governance on all alerts.
Business Impact: A set-and-forget approach that ensures critical alerts reach the correct stakeholders automatically — eliminating manual routing and improving organizational governance.
ML Platform and Model Management
AutoML Training Observability and Catalog Integration
- Real-time progress tracking — current stage and overall completion.
- Feature engineering insights — all features processed and evaluated.
- Hyperparameter transparency — detailed logs of hyperparameters used.
- Failure diagnostics — immediate access to status reports and error logs.
Business Impact: Removes the black-box nature of AutoML, enabling faster debugging, informed model selection, and audit-ready explainability for production deployment.
Enhanced Conda Environment Management
Enhancement: End-to-end visibility into asynchronous Conda environment lifecycles. New capabilities:
- Unified Create status for new and imported environments.
- YAML Preview button to inspect configuration files and execution logs in-UI.
- Detailed failure logs and one-click Retry for failed operations without leaving the screen.
- Validation warnings for imported YAMLs missing an explicit Python version.
Business Impact: Transforms a black-box process into a transparent, self-service workflow — reducing downtime, eliminating backend intervention, and preventing silent execution failures.
User Provisioning and Identity Diagnostics
User provisioning and identity handling now include improved SAML validation, error handling, and diagnostics; stronger user-provisioning and identity-mapping validation; and safer fallback behavior with clearer responses when user or group data is missing or invalid.
Business Impact:Provides clearer validation and diagnostic information for troubleshooting SAML, user-provisioning, and identity-mapping issues.
HTTP Strict Transport Security
HTTP Strict Transport Security (HSTS) has been added to response headers to enforce HTTPS browser behavior.
Business Impact: Enforces HTTPS browser communication through the HSTS response header.
Data Sourcing and Model Upload Diagnostics
Workspace Data Sourcing and model upload processing have been enhanced with clearer object-level execution status and failure visibility, better progress tracking and failure isolation for large uploads and batches, dependency-aware handling and reporting for database objects, improved troubleshooting for partitioned database objects, and separate AAI-DMM service logs.
Business Impact: Provides more granular status and failure information for diagnosing Data Sourcing and model upload operations.
Optional Synonym Creation
Workspace Data Sourcing now supports optional synonym creation.
Business Impact: Allows users to avoid synonym creation when it is not required, supporting the stated objective of reducing storage costs.
Dataset Transformation and DateTime Engineering
- In-view application and preview of transformation logic against live dataset records.
- Automated DateTime normalization across diverse formats for pipeline consistency.
- Dedicated Encode Datetime functionality for ML-ready temporal feature engineering.
Business Impact: Accelerates data preparation by enabling iterative transformations with direct data visibility, eliminating manual overhead and reducing error rates in time-series data cleaning.
Dataset Feature Restriction Removed
The Dataset screen no longer restricts datasets to 100 features.
Business Impact: Allows users to work with datasets containing more than 100 features on the Dataset screen.
Independent Custom SQL Queries
During Step 3 of dataset creation, users can provide complex custom SQL queries independently against a single datastore.
Business Impact: Provides flexibility to define dataset logic with independent custom SQL queries on a single datastore.
Model Pipelines
Open Session Monitoring and Management
Model Pipelines now supports monitoring and managing open sessions.
Business Impact:Provides visibility and management controls for open sessions
Model Monitoring Metadata Management and Persistence
Model Monitoring metadata can be managed and persisted.
Business Impact: Retains Model Monitoring metadata for continued management and use.
Model Monitoring Pipeline View
A Pipeline View is available for Model Monitoring
Business Impact: Provides a pipeline-oriented view of Model Monitoring information.
Model and AI Risk Management (MARM)
- Governed end-to-end workflow: Data Profiling & Drift Analysis, Outcome Analysis & Backtesting, Conceptual Soundness, Tiering, Findings, and Final Review.
- Draft saving, comprehensive validation history tracking, and detailed execution reports.
- Portfolio-level oversight via Dashboard, Model Inventory, Model 360 view, and Validation Calendar.
- Step-level commentary and formal validation decisioning within a strictly governed lifecycle.
Business Impact: Digitizes the model lifecycle to ensure all AI, ML, and Statistical models meet regulatory and internal standards — providing audit-ready executive visibility and mitigating risk associated with model failure.
Model Dashboard
- Clickable KPIs with drill-down into filtered Model Inventory
- Risk-prioritized Critical Validation Backlog for timely attention to validation demand
- Model-risk distribution by business domain and production-model validation status
- Model lifecycle-stage and production-model vintage distribution
Business Impact: Improves oversight of model risk and validation demand by bringing the most relevant inventory, risk, and backlog indicators into a single decision-oriented view.
Model Registration in MARM
Model registration in MARM has been streamlined through two guided paths.
For models already available in MMG, MARM performs a readiness check to confirm that the selected model is ready to enter evaluation or validation.
For external models, a dedicated registration path enables models from external environments to be registered through a streamlined process within MARM.
Business Impact: Provides a clearer, faster path to bring both MMG and external models into the MARM validation lifecycle, while establishing readiness before validation begins.
Model Tiering
Model Tiering now includes automatic tier score calculation, configuration-driven standard tiering criteria, an improved tiering-criteria layout, display of the previous tier, and controlled manual override.
Business Impact: Reduces manual calculation through automatic tier scoring while retaining controlled override and visibility into the previous tier.
Backtesting
Backtesting now supports an As-of Date, allowing validators to select the date as of which date a model is to be validated. Backtesting has also been expanded to probability of default (PD), classification, multiclass, and traditional models.
Business Impact: Enables validation against the relevant historical point in time, helping validator persona capture the real business value of the backtesting process.
Audit Logging
MARM now records validation-related activities as part of the original pipeline audit logs.
Business Impact:Improves traceability by providing a consolidated audit record of validation activity alongside the underlying pipeline history.
Role-Based Access Controls
MARM now supports role-based access controls through three seeded personas: Model Owner, Model Validator, and Lead Validator / Validation Approver. Access is organized through the MMG/OFSAA User-to-User Group-to-Role-to-Function model, with granular view, validation-execution, and approval functions.
Business Impact: Strengthens governance through clear accountability and segregation of duties: Model Owners receive read-only access, Model Validators perform validation work, and Lead Validators / Validation Approvers review and finalize outcomes.
Findings and Observation Tracking
MARM now provides a flow to track findings and observations identified in a validation. Open findings can be carried forward into subsequent validations, enabling validators to continue tracking them until closure.
Business Impact: Provides continuous visibility of unresolved validation matters and supports disciplined follow-through across validation cycles.
Admin and Installation
Infrastructure Observability
Build-monitor components now include configurations for Prometheus, Node Exporter, and Oracle Exporter. Prometheus remains optional, giving teams the flexibility to enable advanced health monitoring based on operational needs.
Proactive Pre-Install Checks
Automated validation added for DTP placeholders, schema connectivity, service ports (DTP, UI, Jobs), and TLS-related configurations — ensuring full environment readiness before deployment.
Security and Hardening
TLS 1.3 implemented with comprehensive CVE remediation. Users can manually configure their TLS version to align with internal security policies.
Authentication and Tech Stack Modernization
Introduced LDAP-based authentication (AuthN) and upgraded to Data Studio 26.1. Java 25 is now supported, with maintained backward compatibility for Java 17 and 21.
Business Impact: Pre-install checks reduce setup time and troubleshooting overhead. TLS 1.3, LDAP support, and Java 25 keep the platform secure and compliant with modern enterprise standards, while optional Prometheus integration enables cost-effective monitoring scalability.
Performance Improvements
Technology Stack
- Data Studio version 26.1.2
- Oracle Database 26.ai support in addition to existing ones
- Java version 17, 21, 25
- RAPID version 20
- OJet version 19.1
All 3rd party libraries upgraded to latest stable versions for improved performance and security.
Parent topic: Introduction