Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

Pei Yin, Wen-long Jiang, Zi-jie Ma and Li-ke Zhang
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Pei Yin: Business School, University of Shanghai for Science and Technology, Shanghai, China & School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai, China
Wen-long Jiang: Business School, University of Shanghai for Science and Technology, Shanghai, China
Zi-jie Ma: Business School, University of Shanghai for Science and Technology, Shanghai, China
Li-ke Zhang: Business School, University of Shanghai for Science and Technology, Shanghai, China

International Journal of Intelligent Information Technologies (IJIIT), 2024, vol. 20, issue 1, 1-21

Abstract: This study introduces an interpretable imbalanced data classification method for detecting cryptocurrency transaction fraud. We address data imbalance using SMOTE oversampling and data augmentation through contrastive learning. Next, we introduce a Transformer-based deep learning model that learns sample relevance. The model undergoes pre-training with a contrastive loss and fine-tuning through Bayesian optimization to effectively extract high-dimensional, higher-order, and fraud-related features. We employ a SHAP-based interpreter along with attention scores to elucidate the role of various transaction features in fraud detection. Comparative results demonstrate the model's remarkable recall performance in identifying cryptocurrency transaction fraud. Furthermore, it achieves an excellent F1 value, striking a balance between accuracy and recall. This research not only enriches financial fraud detection but also enhances cryptocurrency transaction security, promotes market development, and contributes to economic stability and social security.

Date: 2024
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