A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks
This work addresses the need for practical tools in financial supervision and systemic risk analysis, though it is incremental as it builds on existing clustering frameworks with domain-specific adaptations.
The paper tackled the problem of identifying functional roles of financial institutions in multi-layer networks to aid supervision and risk assessment, by proposing an interpretable role-based clustering method that uncovered roles like market intermediaries and cross-segment connectors using ECB transaction data.
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.