# A Multimodal Approach to SME Credit Scoring Integrating Transaction and Ownership Networks

arXiv: https://arxiv.org/abs/2510.09407 · HTML: https://mlsanity.com/paper/2510.09407/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 73 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 22 of 22)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [Multimodal Intelligence: Representation Learning, Information Fusion, and Applications](https://mlsanity.com/paper/1911.03977/) (2019)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics](https://mlsanity.com/paper/2002.09931/) (2020)
- [The value of text for small business default prediction: A deep learning approach](https://mlsanity.com/paper/2003.08964/) (2020)
- [Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams](https://mlsanity.com/paper/2304.10740/) (2023)
- [Multilayer Network Analysis for Improved Credit Risk Prediction](https://mlsanity.com/paper/2010.09559/) (2020)
- [Graph Neural Networks based Log Anomaly Detection and Explanation](https://mlsanity.com/paper/2307.00527/) (2023)
- [Anomaly Detection in Networks with Application to Financial Transaction Networks](https://mlsanity.com/paper/1901.00402/) (2019)
- [A transformer-based model for default prediction in mid-cap corporate markets](https://mlsanity.com/paper/2111.09902/) (2021)
- [Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction](https://mlsanity.com/paper/2402.00299/) (2024)
- [Evolution of Credit Risk Using a Personalized Pagerank Algorithm for Multilayer Networks](https://mlsanity.com/paper/2005.12418/) (2020)
- [Time Series Feature Redundancy Paradox: An Empirical Study Based on Mortgage Default Prediction](https://mlsanity.com/paper/2501.00034/) (2024)
- [Corporate payments networks and credit risk rating](https://mlsanity.com/paper/1711.07677/) (2017)
- [The Banking Transactions Dataset and its Comparative Analysis with Scale-free Networks](https://mlsanity.com/paper/2109.10703/) (2021)
- [Identifying discreditable firms in a large-scale ownership network](https://mlsanity.com/paper/2211.14316/) (2022)

## Abstract (arXiv)

Small and Medium-sized Enterprises (SMEs) are known to play a vital role in economic growth, employment, and innovation. However, they tend to face significant challenges in accessing credit due to limited financial histories, collateral constraints, and exposure to macroeconomic shocks. These challenges make an accurate credit risk assessment by lenders crucial, particularly since SMEs frequently operate within interconnected firm networks through which default risk can propagate. This paper presents and tests a novel approach for modelling the risk of SME credit, using a unique large data set of SME loans provided by a prominent financial institution. Specifically, our approach employs Graph Neural Networks to predict SME default using multilayer network data derived from common ownership and financial transactions between firms. We show that combining this information with traditional structured data not only improves application scoring performance, but also explicitly models contagion risk between companies. Further analysis shows how the directionality and intensity of these connections influence financial risk contagion, offering a deeper understanding of the underlying processes. Our findings highlight the predictive power of network data, as well as the role of supply chain networks in exposing SMEs to correlated default risk.