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SAS® Fraud Network Analysis

Find – and stop – organized fraud by analyzing big data

Stay ahead of organized fraudsters. Catch those who may appear normal, yet operate below the radar. A single platform lets you monitor customer behavior across multiple bank accounts, systems and lines of business.


Gain a greater competitive advantage.

More effective fraud detection methods can actually discourage fraudsters from targeting your organization. And fewer false positives means a better customer experience for legitimate customers – and greater customer satisfaction.

Get a consolidated view of fraud risk.

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

Detect more fraud, and reduce your 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 would otherwise miss. The result? One bank reduced costs by 80% and saved $1.1 million monthly.

Reduce false positives. Boost 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.


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  • Fraud data management. Consolidate historical data from internal and external sources for fraud analysis and investigation.
  • Rule and analytic model management. Create and logically manage business rules, analytical models and alerts of known fraudster lists for investigators.
  • Detection and alert generation. Score data in real time with an online scoring engine that combines business rules, anomaly detection and advanced analytic techniques.
    • Alert management. Combine alerts from multiple monitoring systems, associate them with common data, and give investigators a more complete perspective on risk.
    • Social network analysis. Automatically identify suspicious networked behavior in the data.
    • Optional, integrated case management. Streamline operations with a systematic means of investigations, using a configurable workflow.
    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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