aiqbench analysis
This paper applies interpretable clustering and explainable node embeddings to map functional roles within multi-layer financial networks. By leveraging transaction data from the ECB's Money Market Statistical Reporting system, it identifies distinct institutional behaviors such as market intermediaries and cross-segment connectors using machine learning techniques on social network structures. The work advances systemic risk assessment by providing a flexible framework for analyzing complex trading relationships without relying solely on black-box models.
Interpretable Role-Based Clustering in Multi-Layer Financial Networks
arXiv:2507.00600v3 Announce Type: replace-cross 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.
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.
Social and Information Networks (cs.SI); Machine Learning (cs.LG)
arXiv:2507.00600 [cs.SI]
(or arXiv:2507.00600v3 [cs.SI] for this version)
Full text under the publisher's open licence · 153 words
Related coverage
Interpretable clustering via optimal multi-way decision trees
arXiv · Aug 24, 2026
Beyond Feature Importance: A Comparative Analysis of Pattern Detection Methods in Cluster Interpretation
arXiv · Aug 7, 2026
Cluster LOCO: Feature Importance For Interpreting Clusters
arXiv · Jun 15, 2026
Latent Cluster Analysis for Vision-Language-Action Models
arXiv · Sep 3, 2026