Fraud Network Analysis

SAS® Fraud Network Analysis

Find – and stop – organized fraud with big data

Organized fraud is a growing problem. And while the transaction monitoring systems employed by most banks work well for individual real-time, point-of-sale fraud, a different approach is needed for monitoring customer behavior across multiple accounts and systems in order to identify those that may appear normal, yet operate below the radar. SAS Fraud Network Analysis is the answer.


Detect more fraud and reduce losses.

Spot more suspicious activity by processing all data (not just a sample) through business rules and analytical models in near-real time or batch. A unique visualization interface lets you see linked entities and crime rings that you wouldd otherwise miss.

Get a consolidated view of fraud risk.

Identify cross-brand/product fraud by seeing customer accounts and transactions for all lines of business. And stay on top of trends by continuously improving models and adapting the system.

Reduce false positives while boosting efficiency.

Don't waste time investigating false-positive alerts. A sophisticated fraud scoring engine applies risk- and value-based scoring models to prioritize events before they go to investigators.  With the time saved, investigators can work many times the number of cases and focus on higher-value networks.

Gain a greater competitive advantage.

Provide a better through Fewer false positives means a better customer experience for legitimate customers. And that leads to greater customer satisfaction. More diligent, effective fraud detection methods can actually discourage fraudsters from targeting your organization.



Fraud Network Analysis
  • Fraud data management
  • Rule and analytic model management
  • Detection and alert generation
  • Alert management
  • Social network analysis
  • Optional integrated case management
  • Auditability
We have an excellent working relationship with SAS. They took the time to learn from us and truly understand the nuances of claims fraud at CNA so that we could build effective predictive models for each line of our business.

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