On the combination of graph data for assessing thin-file borrowers’ creditworthiness-Reference-Cited by-同舟云学术

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

Published:2023-03 Issue: Volume:213 Page:118809
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Muñoz-Cancino Ricardo^ORCID,Bravo Cristián^ORCID,Ríos Sebastián A.,Graña Manuel^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference60 articles.

1. User’s guide to correlation coefficients;Akoglu;Turkish Journal of Emergency Medicine,2018

2. Credit intelligence and modelling: Many paths through the forest of credit rating and scoring;Anderson,2022

3. Network embedding: An overview;Arsov,2019

4. Machine learning and AI for risk management;Aziz,2019

5. A credit analysis of the unbanked and underbanked: An argument for alternative data;Baidoo,2020

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2. Improving EMI Risk Model for E-commerce with Customer Embedding Obtained through Heterogeneous Graph;Proceedings of the 2024 16th International Conference on Machine Learning and Computing;2024-02-02

3. A network model of social contacts with small-world and scale-free features, tunable connectivity, and geographic restrictions;Mathematical Biosciences and Engineering;2024

4. Machine Learning for Credit Risk Prediction: A Systematic Literature Review;Data;2023-11-07

5. Predicting the trading behavior of socially connected investors: Graph neural network approach with implications to market surveillance;Expert Systems with Applications;2023-10

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