Fraud Detection & Prevention: A Data-Driven Approach

Fraud Detection & Prevention: A Data-Driven Approach

Altair Knowledge Studio Spotlight Session

On-Demand Webinar Recording Now Available. 

Fraud impacts everyone—from individual consumers to large corporations. 

Traditional rules-based systems may have been effective in the past in identifying fraud, but they become ineffective and stale as fraudsters learn how to bypass those rules. It becomes even more challenging due to the large volumes of data that need to be processed and examined to detect fraud, in addition to the constantly changing tactics for committing fraud – those activities are usually hidden in large volumes of data.

Recently developed machine learning techniques are increasingly effective in detecting fraud with the advances in data systems (e.g. big data, streaming data) and computational systems (e.g. high performance computing, GPU). As a result, it is possible to identify fraudulent patterns of behavior in data that is constantly being captured from day-to-day activities. In addition, it is feasible to address the challenges associated with fraudsters changing their tactics.

During the session, we'll cover how you can: 

  • Stay ahead of fraudsters using predictive insights and real-time monitoring and scoring 
  • Leverage historical data to build predictive models that can better target fraud 
  • Cover both detection and prevention to make faster, more accurate decisions



Tom Zougas
Tom Zougas
VP Data Analytics Services