← Back to all news
arXiv Machine Learning By Lu Han, Xiuying Wang

Integrated Multivariate Segmentation Tree for Heterogeneous Credit Data Analysis in Small- and Medium-Sized Enterprises

Read the original on arXiv Machine Learning →

The paper introduces the Integrated Multivariate Segmentation Tree (IMST), a new framework that combines financial data and textual information for credit evaluation of small- and medium-sized enterprises. IMST transforms text into numerical matrices via matrix factorization, selects key financial features with Lasso regression, and builds a multivariate segmentation tree using Gini or entropy with weakest-link pruning. Experiments on 1,428 Chinese SMEs show an 88.9% accuracy, outperforming baseline decision trees, SVMs, and neural networks while offering better interpretability and computational efficiency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Sep 15

Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data

The paper presents a deep learning credit risk early warning system that fuses heterogeneous data sources, such as transaction behaviors and social networks, using deep neural networks and attention mechanisms. By extracting multidimensional features, the system establishes an early identification mechanism for corporate and individual credit risks. Testing shows that this approach improves the accuracy and timeliness of risk warnings compared to traditional rule‑based engines.

By LiYang Wang (Washington University in St. Louis), Zhen Zhong (Georgetown University), Zhen Tian (University of Glasgow), Keyu Chen (Wuyi University), Keyu Chen (Wuyi University)
More like this →
arXiv Machine Learning
Jul 16

Foundation Models for Credit Risk Prediction: A Game Changer?

arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.

By Bart Baesens, Andreas Goethals, Stefan Lessmann, Simon De Vos, Cristi\'an Bravo, David Martens, Victor Medina-Olivares, Christophe Mues, Maria Oskarsd\'ottir, Seppe vanden Broucke, Tony Van Gestel, Tim Verdonck, Wouter Verbeke
llmscomputer-visionnlpbenchmarks
More like this →
arXiv Machine Learning
Aug 14

AI-Driven Multiscenario Interest Rate Forecasting: A Proof of Concept for Banking Asset Management

arXiv:2608. 12424v1 Announce Type: cross Abstract: This study focuses on developing an AI-supported prototype for multiperspective interest rate forecasting that combines classical econometric models with modern artificial intel-ligence methods.

By Ekkehardt Bauer, Dirk Holl\"ander, Linus Wolff, Christoph Ostermair, Kyrillus Aiad, Joachim Hasebrook
nlp
More like this →