Fraud Analytics: Strategies and Methods for Detection and Prevention

Fraud Analytics versus Predictive Analytics

THERE ARE distinct similarities between fraud analytics and predictive analytics. David Coderre stated that both also have significant differences. With predictive analytics, you can see all of the variables that are derived directly or indirectly to the findings of concern. To gain a better understanding of the data associations or links, we must be able to see them in their entirety.¹

Predictive analytics allows the capability of detecting potential security threats, duplicate payments, establishes crime patterns in areas defined as high crime rate areas, insurance fraud, and credit card fraud. Predictive analytics confirms that fraud is always changing; and therefore methods should as well. It's an exhaustive and mostly reiterative process with built-in flexibilities. Predictive analytics looks for stability and repeatability of its findings. The focus of this chapter is on the three predictive analytic (modeling) processes, the purpose and meaning of each, and how they compare and contrast with fraud analytics and how they relate to fraud detection.

Over the years, both fraud data analysis and predictive analysis (modeling) have been used to detect and predict fraud or suspicious activity (i.e., red flags). They are both very useful tools and are widely used across multiple industries. Fraud data analysis helps to identify behavior, ...