Harnessing logic heterograph learning for financial operational risks: A perspective of cluster and thin-tailed distributions

Abstract

Financial operational risks, alongside credit and market risks, represent a primary concern for commercial banks. However, the inherent complexity of these risks, coupled with the challenges in defining and labeling operational activities, has constrained data-driven analysis in this domain. This study introduces a deep learning framework incorporating operational logic into risk identification through a heterograph embedding network (HEN). The HEN effectively condenses complex, high-dimensional operational logic heterographs into a more manageable low-dimensional space. Within this simplified space, a Density Estimation Network (DEN) is employed to pinpoint risks, drawing on the clustering properties and thin-tailed characteristics of financial data. These components are harmoniously combined through a unified objective function, designed to optimize both dimensionality reduction and classification decisions. Experimental evaluation on a real-world financial dataset reveals that the proposed framework surpasses several cutting-edge algorithms in terms of both practicality and effectiveness. More importantly, our approach is generalizable and can be extended to learn the logical connections between features across various domains.