Complex event processing and pattern recognition-driven AI financial transaction risk monitoring system design

Abstract

Amid the rapid evolution of the digital economy, the increasing complexity, multidimensionality, and diversification of financial transaction modalities have become ever more pronounced, rendering the precise identification and monitoring of financial transaction risks critically imperative. Risk analysis in financial transactions fundamentally constitutes a pattern recognition endeavor, in which the objective is to distill features from complex datasets and detect anomalous patterns, thereby effectively differentiating normal transactions from high-risk ones. Against this backdrop, this study introduces Graph Neural Bidirectional Long Short-Term Memory (Bi-LSTM) Model (GNBLM), a financial transaction risk identification model integrating Graph Neural Networks (GNN), Bi-LSTM, and a temporal attention mechanism, designed to enhance both accuracy and robustness in risk prediction. Initially, the study undertakes comprehensive data preprocessing, encompassing the construction of trading account networks, dataset cleaning, and imputation of missing values in time-series data, followed by the extraction of structured feature information via graph embedding techniques. Subsequently, a GNN is employed to model the intricate interrelations among accounts within the trading network, while a Bi-LSTM captures the dynamic temporal characteristics of time series data. The temporal attention mechanism further amplifies feature representations at critical time points. Leveraging these enriched features, the model classifies transaction risk using a fully connected layer. Experimental evaluations reveal that GNBLM achieves superior predictive accuracy on multiple publicly available financial risk datasets, outperforming contemporary risk assessment approaches and traditional machine learning methods such as Support Vector Machine (SVM). Notably, GNBLM shows remarkable robustness in imbalanced datasets. This research pioneers a novel technological framework for enterprise financial risk management, catalyzes advancements in intelligent transaction risk identification, and establishes a robust foundation for the precision and automation of future financial systems.