  Anti-money laundering (AML) regulations mandate financial institutions to
deploy AML systems based on a set of rules that, when triggered, form the basis
of a suspicious alert to be assessed by human analysts. Reviewing these cases
is a cumbersome and complex task that requires analysts to navigate a large
network of financial interactions to validate suspicious movements.
Furthermore, these systems have very high false positive rates (estimated to be
over 95\%). The scarcity of labels hinders the use of alternative systems based
on supervised learning, reducing their applicability in real-world
applications.
  In this work we present LaundroGraph, a novel self-supervised graph
representation learning approach to encode banking customers and financial
transactions into meaningful representations. These representations are used to
provide insights to assist the AML reviewing process, such as identifying
anomalous movements for a given customer. LaundroGraph represents the
underlying network of financial interactions as a customer-transaction
bipartite graph and trains a graph neural network on a fully self-supervised
link prediction task. We empirically demonstrate that our approach outperforms
other strong baselines on self-supervised link prediction using a real-world
dataset, improving the best non-graph baseline by $12$ p.p. of AUC. The goal is
to increase the efficiency of the reviewing process by supplying these
AI-powered insights to the analysts upon review. To the best of our knowledge,
this is the first fully self-supervised system within the context of AML
detection.
