SAS® for Enterprise Fraud and Financial Crime Features

A hybrid analytic approach

  • Business rules for automatically flagging suspicious activities.
  • Near-real-time transaction monitoring.
  • Anomaly detection for uncovering abnormal patterns of behavior.
  • Predictive modeling for identifying new or emerging threats based on previous threats.
  • Network analysis for linking multiple parties through associative behavior or common ownership.
  • Adaptive segmentation with advanced neural networks.

Entity linking

  • Identifies behavior patterns among customers, accounts or other entities that only appear suspicious when viewed in a larger context across a community of related accounts or entities.
  • Analyzes activities and relationships within a network of related entities, such as customers who share demographic data or transactions.

Correspondent banking scenarios

  • A banking-specific data model includes correspondent banking scenarios to further strengthen detection methods in the high-risk category of correspondent banking.
  • A relationship grid enables fast assessment of details associated with correspondent banking alerts and the entities connected with them.

Peer group anomaly detection

  • Compares an entity’s behavior to its historical behavior, as well as the behavior of its peers.
  • Supports multiple peer groups.
  • Provides outlier detection both above and below expected behavior.

Customer due diligence

  • Rates new customers and updates existing customer scores in three primary categories – entity, geography and product type – based on key events that could affect the overall risk of the relationship.
  • Systematically reviews customer profiles and notifies relevant parties if there are any changes.

High-performance analytics

  • Extracts accurate insights from big data in shorter reporting windows.
  • Applies sophisticated analytic techniques to all relevant data – not just a sample.
  • Array processing enables monitoring multiple risks during a single pass of the data.

Enterprise data management

  • Takes an enterprise approach to data management and consolidation to ensure consistent, accurate, timely data.
  • Combines data integration, data quality and master data management  in a unified environment.
  • Brings together cross-channel enterprise data – from all lines of business, organizational units and geographic regions – on a single platform.

Flexible alert management

  • Flexible interface offers configurable, secure access to multiple solutions – customer due diligence, currency transaction reporting, case management, AML solutions, etc.
  • Assembles alerts from multiple monitoring systems, associates them with common individuals or entities, and automatically prioritizes and routes suspicious cases.
  • Enables efficient review of work items sent electronically to the system prior to creating or linking incidents to a case.

Centralized case management

  • Enables creation of multiple automated workflows for different types of cases – fraud, money laundering, etc.
  • Includes to-do lists and makes certain action items mandatory prior to routing the case to the next step in the workflow.
  • Enables attachment of comments or documentation relevant to a case.
  • Captures and tracks all information relevant to a case through a central audit service, which enables easy auditing of the investigation process.

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