A risk assessment framework for online transactions via Graph Neural Networks and efficient probabilistic prediction

Abstract

Transactions are integral to daily life, but the occurrence of abnormal behaviors can lead to significant risks. Online transaction risk is characterized by the accumulation of abnormal behaviors, where their frequency surpasses a predefined threshold, resulting in measurable probabilities and consequences. Therefore, the assessment of online transaction risk heavily depends on probabilistic predictions of the accumulated frequency of abnormal behaviors, presenting two major challenges. Firstly, abnormal behaviors across different instances (e.g., behavior types, product categories, regions, and platforms) exhibit temporal correlations, such as co-occurrence and concomitance, which most probabilistic models fail to identify and utilize effectively. Additionally, these models do not fully address the real-time demands. To address these challenges, we propose a novel risk assessment framework based on Graph Neural Network (GNN) and probabilistic prediction, named GNN-Probformer. The framework uses Dynamic Time Warping to capture temporal correlations between abnormal behavior frequency sequences and constructs a graph structure through clustering. It then employs Graph Neural Networks to aggregate features and learn representations through a novel embedding module. A sparse self-attention mechanism and an efficient encoder–decoder architecture are incorporated to further enhance performance, while probabilistic predictions are generated through Monte Carlo sampling and cumulative distribution functions. Experimental results on a real-world dataset demonstrate that GNN-Probformer achieves substantial performance gains, with a 15% reduction in normalized deviation. At the 90th percentile, it further reduces normalized quantile loss by 15% and improves the F1-score by 16%, while also reducing training time and inference time by 47% and 38%, respectively.