Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification with Interpretable Analysis

Given the significant distribution disparity between normal and fraudulent transaction data in cryptocurrency samples, as well as the complex high-dimensional nature of transaction data with non-linear relationships, 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. Ablation experiments affirm the necessity of the proposed data balancing and pre-training-fine-tuning strategies, highlighting their effectiveness in addressing imbalanced data classification issues. 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.