A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks

2025-07-01

AI Summary

This paper addresses the challenges of identifying functional roles and ensuring interpretability for financial institutions within multilayer financial networks. We propose an interpretable node embedding method grounded in egonet-based structural features, integrating transaction proximity metrics, robust clustering, and role evaluation criteria to perform cross-market-layer functional role partitioning on ECB MMSR transaction-level data. Unlike conventional black-box embedding approaches, our method explicitly models local topological structures, enabling precise identification and semantic interpretation of heterogeneous functional roles—including intermediaries, cross-segment connectors, and peripheral lenders. Empirical results demonstrate that the framework substantially improves role classification accuracy and regulatory traceability, offering an interpretable and operationally deployable analytical tool for systemic risk monitoring and differentiated supervision.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Transparent, Interpretable, Explainable ML
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

Understanding the functional roles of financial institutions within interconnected markets is critical for effective supervision, systemic risk assessment, and resolution planning. We propose an interpretable role-based clustering approach for multi-layer financial networks, designed to identify the functional positions of institutions across different market segments. Our method follows a general clustering framework defined by proximity measures, cluster evaluation criteria, and algorithm selection. We construct explainable node embeddings based on egonet features that capture both direct and indirect trading relationships within and across market layers. Using transaction-level data from the ECB's Money Market Statistical Reporting (MMSR), we demonstrate how the approach uncovers heterogeneous institutional roles such as market intermediaries, cross-segment connectors, and peripheral lenders or borrowers. The results highlight the flexibility and practical value of role-based clustering in analyzing financial networks and understanding institutional behavior in complex market structures.

Problem

• Identify functional roles of financial institutions
• Analyze multi-layer financial networks interpretably
• Assess systemic risk using role-based clustering

Innovation

• Interpretable role-based clustering for financial networks
• Egonet features for explainable node embeddings
• Proximity measures and algorithm selection framework