Fraud Detection in R

This chapter will first give a formal definition of fraud. You will then learn how to detect anomalies in the type of payment methods used or the time these payments are made to flag suspicious transactions.

In the second chapter, you will learn how to use networks to fight fraud. You will visualize networks and use a sociology concept called homophily to detect fraudulent transactions and catch fraudsters.

Fortunately, fraud occurrences are rare. However, this means that you're working with imbalanced data, which if left as is will bias your detection models. In this chapter, you will tackle imbalance using over and under-sampling methods.

In this final chapter, you will learn about a surprising mathematical law used to detect suspicious occurrences. You will then use robust statistics to make your models even more bulletproof.