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Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection by Wouter Verbeke, Veronique Van Vlasselaer, Bart Baesens

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Chapter 5Social Network Analysis for Fraud Detection

In the last decade, the use of social media websites in everybody's daily life is booming. People can continue their conversations on online social network sites like Facebook, Twitter, LinkedIn, Google+, Instagram, and so on and share their experiences with their acquaintances, friends, family, and others. It only takes one click to update your whereabouts to the rest of the world. Plenty of options exist to broadcast your current activities: by picture, video, geo-location, links, or just plain text. You are on the top of the world—and everybody's watching. And this is where it becomes interesting.

Users of online social network sites explicitly reveal their relationships with other people. As a consequence, social network sites are a (almost) perfect mapping of the relationships that exist in the real world. We know who you are, what your hobbies and interests are, to whom you are married, how many children you have, your buddies with whom you run every week, your friends at the wine club, etc. This whole interconnected network of people knowing each other, somehow, is an extremely interesting source of information and knowledge. Marketing managers no longer have to guess who might influence whom to create the appropriate campaign. It is all there—and that is exactly the problem. Social network sites acknowledge the richness of the data sources they have, and are not willing to share them as such and free of cost. Moreover, ...

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