# On the combination of graph data for assessing thin-file borrowers' creditworthiness

arXiv: https://arxiv.org/abs/2111.13666 · HTML: https://mlsanity.com/paper/2111.13666/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 79 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 23 of 23)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Modeling Event Propagation via Graph Biased Temporal Point Process](https://mlsanity.com/paper/1908.01623/) (2019)
- [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)
- [Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications](https://mlsanity.com/paper/2005.14658/) (2020)
- [Multilayer Network Analysis for Improved Credit Risk Prediction](https://mlsanity.com/paper/2010.09559/) (2020)
- [Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture](https://mlsanity.com/paper/2001.06701/) (2020)
- [Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data](https://mlsanity.com/paper/2001.08427/) (2020)
- [EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data](https://mlsanity.com/paper/2009.14588/) (2020)
- [Credit Scoring for Good: Enhancing Financial Inclusion with Smartphone-Based Microlending](https://mlsanity.com/paper/2001.10994/) (2020)
- [Evolution of Credit Risk Using a Personalized Pagerank Algorithm for Multilayer Networks](https://mlsanity.com/paper/2005.12418/) (2020)
- [Network Embedding: An Overview](https://mlsanity.com/paper/1911.11726/) (2019)
- [A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry](https://mlsanity.com/paper/2001.06700/) (2020)
- [Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data](https://mlsanity.com/paper/2102.09974/) (2021)

## Built on by (most cited first, 4 of 4)

- [On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance](https://mlsanity.com/paper/2204.06122/) (2022)
- [Assessment of creditworthiness models privacy-preserving training with synthetic data](https://mlsanity.com/paper/2301.01212/) (2022)
- [Variational Graph Convolutional Neural Networks](https://mlsanity.com/paper/2507.01699/) (2025)
- [Enhancing Credit Risk Prediction: A Multi-stage Ensemble Pipeline](https://mlsanity.com/paper/2509.22381/) (2025)

## Abstract (arXiv)

The thin-file borrowers are customers for whom a creditworthiness assessment is uncertain due to their lack of credit history; many researchers have used borrowers' relationships and interactions networks in the form of graphs as an alternative data source to address this. Incorporating network data is traditionally made by hand-crafted feature engineering, and lately, the graph neural network has emerged as an alternative, but it still does not improve over the traditional method's performance. Here we introduce a framework to improve credit scoring models by blending several Graph Representation Learning methods: feature engineering, graph embeddings, and graph neural networks. We stacked their outputs to produce a single score in this approach. We validated this framework using a unique multi-source dataset that characterizes the relationships and credit history for the entire population of a Latin American country, applying it to credit risk models, application, and behavior, targeting both individuals and companies. Our results show that the graph representation learning methods should be used as complements, and these should not be seen as self-sufficient methods as is currently done. In terms of AUC and KS, we enhance the statistical performance, outperforming traditional methods. In Corporate lending, where the gain is much higher, it confirms that evaluating an unbanked company cannot solely consider its features. The business ecosystem where these firms interact with their owners, suppliers, customers, and other companies provides novel knowledge that enables financial institutions to enhance their creditworthiness assessment. Our results let us know when and which group to use graph data and what effects on performance to expect. They also show the enormous value of graph data on the unbanked credit scoring problem, principally to help companies' banking.