Machine Learning Applied to Bank Fraud Detection

EasyChair Preprint 15523, version 2

Abstract

Online payment fraud has been steadily increasing in recent years.
Our focus is on installment payments for e-commerce, which pose a significant risk of customers failing to repay the full amount owed.
To manage this risk, BNP Paribas Personal Finance has developed a system that combines graph databases and artificial intelligence, achieving a 20\% reduction in fraud.
In this article, we propose an extension of this system using a graph neural network (GraphSAGE) combined with an ensemble method (such as Random Forest or XGBoost).
We demonstrate the performance improvements of this combined approach over the initial system using a real anonymized dataset made available to the community.

Keyphrases: Détection de fraudes, Financial Fraud Detection, GNN, Graph Neural Networks, apprentissage machine, detection de fraudes, graph representation learning

@booklet{EasyChair:15523,
  author    = {Aurélien Facci and Bruno Pinaud and Julie Cavarroc and Angelina Pidash},
  title     = {Machine Learning Applied to Bank Fraud Detection},
  howpublished = {EasyChair Preprint 15523},
  year      = {EasyChair, 2024}}