SAS^® Anti-Money Laundering Features

Data management

• A banking-specific data model:
  • Maps transaction records to support transaction, account, customer and household dimensions.
  • Includes the core schema for preparing data for nightly batch analysis.
• A knowledge center data schema supports data retention and investigation.
• Support for multiple data types (nonmonetary event data, geographic data, risk lists, third-party data, associate data and a variety of customer information data) in addition to transaction data.

High-performance analytics & visualization

• Visualize emerging risks.
• Scenario builder enables testing, tuning and simulating scenarios in seconds to improve scenario efficacy and adhere to model governance best practices.
• Provides rapid access to customer, account and transaction data.
• Uses array processing for alert generation.
• Uses peer-group anomaly detection.

Suspicious activity monitoring & reporting

• Applies scenarios and risk factors to transactions to detect suspicious activity.
• Generates alerts for events that meet rule parameters.
• Subjects alerts to additional workflow processes (suppression, risk scoring, routing).
• Includes an easy-to-use, point-and-click interface that enables:
  • Creation and modification of scenarios and risk factors.
  • Creation of customized routing rules for workload distribution.
• Flexible suppression capabilities.

Watch-list matching

• Ability to import sanctions and other watch lists to identify persons, organizations or high-risk jurisdictions that represent regulatory risk.
• Fuzzy-matching logic increases the accuracy of entity matches.
• Ability to work transaction, party or counterparty matching lists as alerts or cases.
• Combines functionality with the Dow Jones Watchlist service.
  

Investigation & alert management

• Aggregation of alerts at the entity level that provide a holistic review of the risk presented by the entity to the institution.
• The number of alerts and aggregation of triggering transactions are displayed for each alerted entity.
• Layout for alerted entity and case queues allows for bulk actions.
• Flat navigation was designed to create user efficiencies and speed the decision-making process.
  

Peer-group anomaly detection

• Compare an entity's current behavior with its historical behavior, as well as the behavior of its peers.
• Include multiple peer groups and detect outliers both above and below expected behavior.
  

Search

• Ability to index and analyze data quickly.
• Gives the user the ability to search comments and attachments.
• Was used by a team of investigative journalists to compile information provided by the Panama Papers to build stories against political figures.

Multitenancy

• Partitioning data by allowing for separate schemas within a database.
• Allowing each schema to have its own AGP process.
• One web application for all schemas means easier management.
• Simplified implementation.