Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation (2412.18287v1)

Abstract: Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.

• The paper presents a novel graph framework that models temporal transactions and integrates attribute-driven risk embeddings for fraud detection.
• The methodology leverages both labeled and unlabeled data, reducing reliance on manual annotation while achieving high AUC and F1 scores.
• Experimental results on real-world and public datasets demonstrate that the proposed model outperforms state-of-the-art benchmarks in accuracy and robustness.

Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

The paper "Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation" proposes a novel approach for detecting fraud in credit card transactions using a semi-supervised graph neural network model. This paper is authored by researchers from multiple institutions, including the Australian Artificial Intelligence Institute at the University of Technology Sydney, Tongji University, and Tencent Jarvis Lab. Their main objective is to leverage both labeled and unlabeled data to improve fraud detection performance significantly, a critical need given the limited availability of labeled transaction data in real-world scenarios.

Key Contributions

1. Temporal Transaction Graph Modeling: The paper introduces a temporal transaction graph that models transactions as nodes and interactions between them as edges. This graph-based representation captures the temporal dynamics inherent in credit card transactions, which are critical for identifying fraudulent patterns.
2. Gated Temporal Attention Network (GTAN): A core component of the proposed framework is the Gated Temporal Attention Network, which utilizes attention mechanisms to learn transaction representations efficiently. The network aggregates information over temporal links thus capturing the evolving behavior of transactions over time — something traditional models might overlook.
3. Attribute-Driven Representation: The authors highlight the importance of including categorical attributes in the modeling process. Traditional methods often overlook these categorical attributes, but the proposed solution integrates them through an attribute-driven learning framework. An attribute learning layer processes transaction attributes as well as a new category of attributes known as risk embeddings, which represent the known fraud status of certain transactions.
4. Risk Embedding and Propagation: Interesting within the paper is the integration of risk embedding into the graph neural network framework. The authors employ a strategy to propagate risk information through the transaction graph, allowing the model to learn fraud patterns from neighboring transactions even when labels are sparse.

Results and Implications

The proposed GTAN was evaluated on a real-world dataset and two publicly available fraud detection datasets. The experimental results demonstrate that the GTAN consistently outperforms state-of-the-art benchmarks across several metrics. Particularly noteworthy are the high AUC and F1 scores, indicating superior detection accuracy and robustness, even with a limited amount of labeled data.

The semi-supervised paradigm employed in GTAN is particularly impactful, significantly reducing the dependency on labeled data. This aspect is crucial for practical deployments because the manual labeling of transaction data is not only expensive but often lagged in comparison to the rate at which transactions are carried out.

Future Directions

The proposed model opens several avenues for further research and enhancements. Future work could explore dynamic graph construction methods that more finely tune the graph structure to evolving fraud tactics. Additionally, further improvements can be made by integrating advanced temporal learning modules to enhance long-term fraud pattern recognition.

The application of such graph-based methods could extend beyond fraud detection, potentially benefiting any domain where relational and temporal data are prevalent. This approach aligns with the increasing trend of using representation learning on graphs for complex prediction tasks.

In conclusion, this paper advances the field of financial fraud detection by effectively utilizing both labeled and unlabeled transaction data through a novel graph neural network approach. As fraud tactics evolve and data continues to grow exponentially, approaches like this one provide a scalable and effective solution for real-time fraud detection.