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

TL;DR

This paper introduces an interpretable clustering method for multi-layer financial networks that identifies institutions' roles, aiding systemic risk analysis and supervision with explainable embeddings derived from transaction data.

Contribution

It presents a novel, explainable role-based clustering framework tailored for multi-layer financial networks, enhancing understanding of institutional functions across market segments.

Findings

Uncovered heterogeneous roles like intermediaries and connectors.

Demonstrated the method's flexibility on ECB transaction data.

Provided insights into institutional behavior in complex markets.

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.