Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023)

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Machine Learning in Home Equity Risk Management: Unbanked Population Credit Assessment

Authors
Yitian Zhang1, *, Parsa Moghaddamcharkari1
1University of Toronto, Toronto, Ontario, Canada
*Corresponding author. Email: yitian.zhang@mail.utoronto.ca
Corresponding Author
Yitian Zhang
Available Online 28 September 2023.
DOI
10.2991/978-94-6463-264-4_58How to use a DOI?
Keywords
Credit Risk Assessment; Logistic Regression; Random Forest; Gradient Boosted Tree; Neural Network
Abstract

This study leverages an imbalanced dataset provided by a home equity company to assess unbanked population’s repayment ability. The target variable is whether the client has repayment difficulties, and independent variables include demographic information and credit history. Logistic regression model and other machine learning models are constructed for comparison. It is found that the neural network model has the best overall performance. Also, clients who are reachable by phone, or have been employed for a longer period in the past are less likely to have repayment difficulties. On the other hand, older clients or whose permanent address does not match their contact address or highest education attended is secondary education would have a higher probability of having repayment difficulties.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
28 September 2023
ISBN
978-94-6463-264-4
ISSN
2589-4900
DOI
10.2991/978-94-6463-264-4_58How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yitian Zhang
AU  - Parsa Moghaddamcharkari
PY  - 2023
DA  - 2023/09/28
TI  - Machine Learning in Home Equity Risk Management: Unbanked Population Credit Assessment
BT  - Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023)
PB  - Atlantis Press
SP  - 510
EP  - 517
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-264-4_58
DO  - 10.2991/978-94-6463-264-4_58
ID  - Zhang2023
ER  -