Using Graph Bayesian Neural Networks for fraud pattern detection and classification from bank transactions data

Abstract

The thesis aims to conduct an investigation into money laundering networks by synthesizing many earlier methods. The methodology begins by training a Bayesian graph neural network on a data set from DNB to learn the relationship between the money laundering status of a transacting party and its features and network properties. One can then get node embeddings by extracting the activation values of each prediction at the last hidden layer in the model. This will hopefully yield informative node embeddings that one could then visualize along with their predicted class and associated uncertainty by applying dimensionality reduction through Principal Component Analysis. If there are nodes that have the same predicted class and magnitude of uncertainty that cluster together, one could try retrieving their associated networks and investigate the patterns.