Fraudulent activities are of critical concern for many entities including financial organizations, insurance companies, governmental institutions, and individual investors. Both public and private projects have dark corners where corrupt actors can hide and thrive. Many contemporary organizations have been stepping up their efforts to detect and prevent fraud. However, spotting fraud patterns and clues can be quite complex most of the time. Thus, many entities still see fraud as a needle in the haystack. Data analytics, as it applies to fraud examination, refers to the use of analytics software to identify trends, patterns, anomalies, and exceptions within data. It is especially useful when fraud is hidden in large data volumes and manual checks are insufficient and can be used both reactively and proactively.