# Advanced spectral clustering for heterogeneous data in credit risk monitoring systems

arXiv: https://arxiv.org/abs/2509.00546 · HTML: https://mlsanity.com/paper/2509.00546/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Ultra-Scalable Spectral Clustering and Ensemble Clustering](https://mlsanity.com/paper/1903.01057/) (2019)
- [Convex Sparse Spectral Clustering: Single-view to Multi-view](https://mlsanity.com/paper/1511.06860/) (2015)
- [Fighting Sampling Bias: A Framework for Training and Evaluating Credit Scoring Models](https://mlsanity.com/paper/2407.13009/) (2024)
- [Construction of the similarity matrix for the spectral clustering method: numerical experiments](https://mlsanity.com/paper/1904.11352/) (2019)
- [Neural Normalized Cut: A Differential and Generalizable Approach for Spectral Clustering](https://mlsanity.com/paper/2503.09260/) (2025)
- [Efficient Semidefinite Spectral Clustering via Lagrange Duality](https://mlsanity.com/paper/1402.5497/) (2014)
- [Multiclass spectral feature scaling method for dimensionality reduction](https://mlsanity.com/paper/1910.07174/) (2019)

## Abstract (arXiv)

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 DeltaIntra/Inter < 0.13 and DeltaSilhouette 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.