Systematic Approach to Enhance Explainable Memory-Augmented Hybrid Graph Attention Framework for Intelligent Banking Risk Prediction

Abstract

Intelligent prediction of banking risk has drawn more attention with the emergence of the digitization of financial systems due to increasing threats from financial fraud. Standard machine learning and deep learning frameworks have struggled to identify hidden relational dependencies and behavioral patterns in customer datasets in the banking domain. To address the limitations of existing frameworks, an Explainable Memory-Augmented Hybrid Graph Attention Intelligent Framework is designed for intelligent banking risk prediction. In this framework, the following components are included for effective risk prediction: dense feature learning for banking data, semantic graph attention for identifying relational dependencies in banking data, memory augmentation for recalling historical representations of banking risks, and explainable attention-based fusion. A comparative study of the proposed framework with the latest approaches, like Artificial Neural Network, Random Forests, Support Vector Machines, Graph Convolutional Networks, and Graph Attention Networks, reveals the superiority of the proposed model, with 90.81% classification performance and an ROC-AUC of 0.940.