Attention based graph model improves credit card fraud detection accuracy
An Attention-Driven Heterogeneous GNN Model for Credit Card Fraud Detection
Machine Learning
Summary
Credit card fraud is hard to catch because fraudsters keep changing how they act, and there are many more normal transactions than fraudulent ones. The authors built a new system that balances the data and uses a special type of graph-based neural network with attention techniques to better understand transaction patterns and user behaviors over time. This approach helped the system accurately spot fraud while reducing mistakes. Their system performed very well on a standard dataset used for testing fraud detection.
What this means in practice
- •For financial fraud detection teams: Detect fraudulent credit card transactions more accurately by using a graph-based model that considers user behaviors and temporal relations with improved data balancing.
- •For payment processor security teams: Enhance fraud detection systems by integrating attention-driven graph models to reduce false positives while capturing complex transaction patterns.
Tested on one dataset.
Authors
Kathiresan Jayabalan, Sethuraman Radhakrishnan
Abstract
The global transition to a cashless economy has placed credit cards as the key element of digital transactions, acclaimed for their easy use, speed, and acceptance in most places. However, the growing dependence on this payment method has led to an escalation of the risks associated with credit card (CC) fraud. Detecting this type of fraud is a difficult task because the patterns are constantly changing, there is a data imbalance, and it is necessary to identify the legitimate transactions and the fraud ones at the same time. This study addresses this challenge by proposing a credit card fraud detection (CCFD) framework using a data balancing technique and a deep learning (DL) model. The proposed fraud detection model is trained and evaluated by collecting the dataset called Credit Card Fraud Detection from the Kaggle repository. As the dataset is highly imbalanced, we utilized the Synthetic Minority Oversampling Technique (SMOTE)-Tomek technique to balance the dataset. Further, the balanced dataset is classified using the Heterogeneous Graph Neural Network (HGNN) model. The HGNN model represent various transactions using a heterogeneous graph architecture and by using an attention based message passing technique, it managed to consider the complex relationships, time factors, and user behavior. The integration of SMOTE-Tomek in the model further boosted its capacity to identify fraudulent transactions, while lowering the rate of false positives. The HGNN model attained a 99.97% accuracy, a 99.48% F1-score, a 99.15% precision, and a 98.97% recall. The findings indicates that this model is effective and can be applied to real-world CCFD scenarios.