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.

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