Advanced spectral clustering for heterogeneous data in credit risk monitoring systems

• URL: http://arxiv.org/abs/2509.00546v1
• Date: Sat, 30 Aug 2025 16:06:00 GMT
• Title: Advanced spectral clustering for heterogeneous data in credit risk monitoring systems
• Authors: Lu Han, Mengyan Li, Jiping Qiang, Zhi Su,
• Abstract summary: We propose Advanced Spectral Clustering (ASC) to identify meaningful clusters in Heterogeneous Data.<n>By bridging spectral clustering theory with heterogeneous data applications, ASC enables the identification of meaningful clusters, such as recruitment-focused SMEs exhibiting a 30% lower default risk.
• Score: 8.92280593592798
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Heterogeneous data, which encompass both numerical financial variables and textual records, present substantial challenges for credit monitoring. To address this issue, we propose Advanced Spectral Clustering (ASC), a method that integrates financial and textual similarities through an optimized weight parameter and selects eigenvectors using a novel eigenvalue-silhouette optimization approach. Evaluated on a dataset comprising 1,428 small and medium-sized enterprises (SMEs), ASC achieves a Silhouette score that is 18% higher than that of a single-type data baseline method. Furthermore, the resulting clusters offer actionable insights; for instance, 51% of low-risk firms are found to include the term 'social recruitment' in their textual records. The robustness of ASC is confirmed across multiple clustering algorithms, including k-means, k-medians, and k-medoids, with {\Delta}Intra/Inter < 0.13 and {\Delta}Silhouette Coefficient < 0.02. By bridging spectral clustering theory with heterogeneous data applications, ASC enables the identification of meaningful clusters, such as recruitment-focused SMEs exhibiting a 30% lower default risk, thereby supporting more targeted and effective credit interventions.

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