A Semi-supervised Graph Attentive Network for Financial Fraud Detection

Abstract: With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection. To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semi-supervised attentive graph neural network, namedSemiGNN to utilize the multi-view labeled and unlabeled data for fraud detection. Moreover, we propose a hierarchical attention mechanism to better correlate different neighbors and different views. Simultaneously, the attention mechanism can make the model interpretable and tell what are the important factors for the fraud and why the users are predicted as fraud. Experimentally, we conduct the prediction task on the users of Alipay, one of the largest third-party online and offline cashless payment platform serving more than 4 hundreds of million users in China. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the state-of-the-art methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.

• The paper introduces SemiGNN, a graph attentive network that propagates labels in complex financial transaction networks using hierarchical attention.
• It employs both node- and view-level attention mechanisms, achieving superior accuracy and AUC compared to traditional methods.
• The results suggest that integrating semi-supervised learning with graph neural networks enhances interpretability and performance in fraud detection.

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

Introduction

The paper "A Semi-supervised Graph Attentive Network for Financial Fraud Detection" (2003.01171) addresses the challenging problem of financial fraud detection using a semi-supervised approach based on graph neural networks (GNNs). Financial services generate complex interaction networks among users, which conventional rule-based fraud detection systems struggle to exploit effectively, particularly when only a small portion of the data is labeled. This paper proposes a novel model, SemiGNN, which leverages a semi-supervised learning framework to utilize both labeled and unlabeled data, thereby enhancing fraud detection performance.

Model Architecture

The core innovation of this work is the introduction of SemiGNN, a graph neural network utilizing hierarchical attention mechanisms to effectively process multiview network data. The model is designed to propagate labels from a scarce number of labeled nodes to a larger set of unlabeled nodes through social relations and multifaceted user data. By incorporating attention mechanisms, SemiGNN can assign varying degrees of importance to different neighbors and data views, which facilitates the interpretation of model predictions and identification of critical factors contributing to detected fraud.

SemiGNN: A Technical Overview

1. Graph Representation: The network is represented as a graph where nodes correspond to users and edges capture their interactions. Each node is associated with multiple views that represent various attributes and interactions.
2. Hierarchical Attention Mechanism: This involves two levels of attention:
   • Node-Level Attention: Identifies which neighboring nodes are most influential in predicting fraudulent activity.
   • View-Level Attention: Determines which view of the data (e.g., transaction activities vs. social interactions) is most significant in the context of fraud detection.
3. Semi-supervised Learning Framework: By integrating a semi-supervised learning approach, SemiGNN effectively utilizes both labeled and unlabeled nodes to improve the model's predictive accuracy and generalization capability.

Experimental Evaluation

The empirical evaluation was conducted using data from Alipay, a major online payment platform in China. The experiments demonstrate that SemiGNN surpasses existing state-of-the-art methods in fraud detection accuracy on this dataset. Significant results include:

• Performance Metrics: The model achieved superior accuracy and AUC compared to baseline models, demonstrating its robustness in leveraging both user social relations and multifaceted attributes.
• Interpretability: The incorporated attention mechanisms provide insights into the model's decision-making process by highlighting key nodes and views responsible for classification outcomes. This interpretability helps stakeholders understand the rationale behind fraud predictions.

Implications and Future Work

The proposed approach extends the applicability of GNNs to fraud detection, emphasizing the value of integrating semi-supervised learning and attention mechanisms in handling sparsely labeled, complex network data. This framework can be adapted for other domains where similar conditions exist, such as cybersecurity or anomaly detection in large-scale networks.

Future research directions include exploring additional graph-based learning architectures and extending the model's applicability to dynamic graphs, where user interactions evolve over time. The scalability of the model could be further enhanced by incorporating distributed computing techniques, opening avenues for real-time fraud detection over extensive financial networks.

Conclusion

This paper presents a significant advancement in financial fraud detection by introducing a semi-supervised graph attentive network. The SemiGNN model effectively exploits both labeled and unlabeled data, and its hierarchical attention mechanisms offer enhanced interpretability and improved detection performance. The findings suggest promising future directions for leveraging graph-based models in complex data environments beyond financial networks.