When it comes to big data, challenges derive from the nature and the volume of the data. Whether it is a data leak or a financial company’s internal data, the amount of data we are dealing with is considerable. To complicate things, investigations usually start from raw, unstructured data. And it’s impossible to automate or scale the investigation without a predefined-data model or any kind of organizational logic.
Organizations across industries are adopting graph analytics to reinforce their anti-fraud programs. Most anti-fraud applications are able to connect simple data points together to detect suspicious behavior. But these applications fall short on more complex analysis. The graph databases we’ve seen emerge in the recent years are designed for this purpose. In this post, we examine three types of fraud graph analytics that can help investigators combat insurance fraud, credit card fraud, and VAT fraud.