SAS^®Anti-Money Laundering

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.

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.

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.

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.

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.

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.

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.