Applying supervised and unsupervised fraud approaches to fraud detection
As we already discussed at the beginning of this chapter, transactions are represented by edges, and we then want to classify each edge in the correct class: fraudulent or genuine. The pipeline we will use to perform the classification task is the following:
- A sampling procedure for the imbalanced task
- The use of an unsupervised embedding algorithm to create a feature vector for each edge
- The application of supervised and unsupervised machine learning algorithms to the feature space defined in the previous point
Dataset resampling
Since our dataset is strongly imbalanced, with fraudulent transactions representing 2.83% of total transactions, we need to apply some techniques to deal with unbalanced data. In this use case, we will apply a simple random undersampling strategy. Going into more depth, we will take a subsample of the majority class (genuine transactions) to match...
