# Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data

arXiv: https://arxiv.org/abs/2102.09974 · HTML: https://mlsanity.com/paper/2102.09974/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Topology Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/1710.10370/) (2017)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [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)
- [Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications](https://mlsanity.com/paper/2005.14658/) (2020)
- [Evolution of Credit Risk Using a Personalized Pagerank Algorithm for Multilayer Networks](https://mlsanity.com/paper/2005.12418/) (2020)
- [Private Information, Credit Risk and Graph Structure in P2P Lending Networks](https://mlsanity.com/paper/1802.10000/) (2018)
- [InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance](https://mlsanity.com/paper/2003.02833/) (2020)
- [NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay](https://mlsanity.com/paper/2004.00201/) (2020)
- [Friendship is All we Need: A Multi-graph Embedding Approach for Modeling Customer Behavior](https://mlsanity.com/paper/2010.02780/) (2020)

## Built on by (most cited first, 1 of 1)

- [On the combination of graph data for assessing thin-file borrowers' creditworthiness](https://mlsanity.com/paper/2111.13666/) (2021)

## Abstract (arXiv)

The presence of Super-Apps have changed the way we think about the interactions between users and commerce. It then comes as no surprise that it is also redefining the way banking is done. The paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior. To this end, two experiments with different graph-based methodologies are proposed, the first uses graph based features as input in a classification model and the second uses graph neural networks. Our results show that variables of centrality, behavior of neighboring users and transactionality of a user constituted new forms of knowledge that enhance statistical and financial performance of credit risk models. Furthermore, opportunities are identified for Super-Apps to redefine the definition of credit risk by contemplating all the environment that their platforms entail, leading to a more inclusive financial system.