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.
The market shares of graph databases keep increasing, as well as the number of products on the market, with seven times more vendors than 5 years ago. Although it seems difficult to agree on exact figures, all reports identify the same growth drivers. In this article, I present the current market, if not exhaustively, at least as well as possible. I divided the graph ecosystem into three main layers, even though the reality is more complex and these stratum are often permeable.
The field of graph theory has spawned multiple algorithms on which analysts can rely on to find insights hidden in graph data. This article covers the graph analytics landscape. Graph analytics, or computing, frameworks. They consist of a set of tools and methods developed to extract knowledge from data modeled as a graph. They are crucial for many applications because processing large datasets of complex connected data is computationally challenging.
Visualization tools represent an important bridge between graph data and analysts. It helps surface information and insights leading to the understanding of a situation, or the solving of a problem. Graph visualization tools turn connected data into graphical network representations that take advantage of the human brain proficiency to recognize visual patterns and more pattern variations. Graph visualization brings many advantages to the analysis of graph data. When you apply visualization methods to data analysis, you are more likely to cut the time spent looking for information.