chapter ten

10 Fighting fraud: Social network analysis

 

This chapter covers:

  • The use of social network analysis (SNA) to classify fraudsters and fraud risks
  • The description of different graph algorithms for SNA-based fraud analytics
  • How to use a real graph database for performing a proper SNA

In this chapter you will learn about techniques for combating fraud that approach the task from a different perspective. The techniques for fighting fraud presented in the previous two chapters use different graph construction methods to create networks based on the information available in the transactions themselves and/or the users’ accounts. In chapter 8, we created a graph connecting users with merchants by using transaction information, and we explored connecting nodes based on overlapping information (two accounts that share an email address, for instance). In chapter 9 you learned how to construct a new kind of graph (the k-NN graph) by computing distances between pairs of observations (each of which has been converted into a node) and storing the top k relationships.

10.1        Social network analysis concepts

Exercise

10.2        Score-based methods

10.2.1                  Neighborhood metrics

Exercise

Exercise

10.2.2                  Centrality metrics

Exercise

10.2.3                  Collective inference algorithms

Exercise

10.3        Cluster-based methods

10.4        Advantages of graphs

10.5        Summary

10.6        References