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.