Fraud Detection and Prevention: A Data-Driven Approach

Fraud impacts everyone—including government agencies.

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 analysis

• Leverage unstructured data and enable predictive models to reduce false positives

• Move from detection to prevention and make faster, more accurate decisions