Graph Neural Networks for Fraud Detection in Large-Scale Banking Networks

Abstract

The amount and complexity of digital banking and financial services transactions make fraud detection tougher than ever. Decision trees and logistic regression ignore fraud scheme variety. By studying complex node interactions, graph neural networks (GNNs) may tackle these issues. Standard methods may overlook hidden links and questionable transactions in large financial networks, suggesting fraud. Bank accounts, transactions, and users are GNN network nodes and edges. Topological node interactions may teach GNNs structural sensitivity.
The study begins with graph neural network building and graph-structured input processing. Smart GNNs like graph convolutional networks and graph attention networks identify fraud. Next, GNNs' local and global transaction network patterns may reveal problems. Find complex, multi-stage scam with several perpetrators planning to hide. 

Studying GNN-ready transactional and network graph topologies. Issues include data representation, graph design for real-world financial transactions, heterogeneous metadata, transaction history, and user behaviour data integration. Embedding and node feature augmentation are examined to increase GNN performance and fraud detection.
This research must contrast GNN-based fraud detection with machine learning and graph-based methods. In rigorous real-world financial dataset testing, GNNs surpass other fraud scheme detection and generalisation approaches. GNNs identify complicated transaction and entity network non-linear fraud. 

This research examines major banks' large-scale GNN adoption and impacts. Training deep GNNs on large networks, optimising graph-specific hyperparameters, and handling dynamic graphs with changing topologies are computationally demanding. Solutions include distributed computing frameworks, sampling for efficient training, and adaptive graph learning algorithms for network temporal changes. 

Ethics and regulations govern GNN model transparency and explainability in fraud detection. The research examines how interpretability increases confidence and fulfils banking regulations. GNN-based suspicious transaction signals are explained to stakeholders via subgraph analysis and feature attribution. 

I finish with graph-based fraud detection research goals. We are studying hybrid models using GNNs and reinforcement learning for real-time adaptive detection and federated learning frameworks for inter-institutional privacy-preserving cooperation. Temporal graph networks or transfer learning can monitor transactional conduct or identify additional networks, according to studies. 

This work reveals that GNNs can represent complex links and identify fraud with unparalleled accuracy and complexity. GNNs detect large-scale financial network fraud utilising machine learning, network theory, and data analytics. Research shows GNNs enhance financial technology and risk management.