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Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques

This paper explores the potential of 19 machine learning techniques to model and forecasts the risk of delinquency on mortgage loans. These techniques include variants of artificial neural networks (ANN), ensemble classifiers, support vector machine, K-nearest neighbors, and decision trees. ensemble...

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Main Authors: Azhar Ali, S.E., Rizvi, S.S.H., Lai, F., Faizan Ali, R., Ali Jan, A.
Format: Article
Published: University of Novi Sad 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102691339&doi=10.24867%2fIJIEM-2021-1-272&partnerID=40&md5=6ad2d7eca1c90281cb5768c36e41c625
http://eprints.utp.edu.my/23739/
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spelling my.utp.eprints.237392021-08-19T10:02:06Z Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques Azhar Ali, S.E. Rizvi, S.S.H. Lai, F. Faizan Ali, R. Ali Jan, A. This paper explores the potential of 19 machine learning techniques to model and forecasts the risk of delinquency on mortgage loans. These techniques include variants of artificial neural networks (ANN), ensemble classifiers, support vector machine, K-nearest neighbors, and decision trees. ensemble classifiers variants. Our dataset comprises 14,062 mortgage loans that have been approved by bank underwriters in the US. We find that Multi-Layer Perceptron (MLP), a variant of ANN, outperforms all other techniques in training time and the precision for testing and training. We have also compared Artificial Neural Network-Multilayer Perceptron (ANN-MLP) results with the traditional binary logistic regression technique's findings. The comparison shows that the ANN-MLP behaves better than the binary logistic regression technique. The study suggests that ANN-MLP could be a valuable extension towards developing the existing toolkit, banks and regulators have to predict delinquency risk on mortgage loans. © 2021. All Rights Reserved. University of Novi Sad 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102691339&doi=10.24867%2fIJIEM-2021-1-272&partnerID=40&md5=6ad2d7eca1c90281cb5768c36e41c625 Azhar Ali, S.E. and Rizvi, S.S.H. and Lai, F. and Faizan Ali, R. and Ali Jan, A. (2021) Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques. International Journal of Industrial Engineering and Management, 12 (1). pp. 1-13. http://eprints.utp.edu.my/23739/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This paper explores the potential of 19 machine learning techniques to model and forecasts the risk of delinquency on mortgage loans. These techniques include variants of artificial neural networks (ANN), ensemble classifiers, support vector machine, K-nearest neighbors, and decision trees. ensemble classifiers variants. Our dataset comprises 14,062 mortgage loans that have been approved by bank underwriters in the US. We find that Multi-Layer Perceptron (MLP), a variant of ANN, outperforms all other techniques in training time and the precision for testing and training. We have also compared Artificial Neural Network-Multilayer Perceptron (ANN-MLP) results with the traditional binary logistic regression technique's findings. The comparison shows that the ANN-MLP behaves better than the binary logistic regression technique. The study suggests that ANN-MLP could be a valuable extension towards developing the existing toolkit, banks and regulators have to predict delinquency risk on mortgage loans. © 2021. All Rights Reserved.
format Article
author Azhar Ali, S.E.
Rizvi, S.S.H.
Lai, F.
Faizan Ali, R.
Ali Jan, A.
spellingShingle Azhar Ali, S.E.
Rizvi, S.S.H.
Lai, F.
Faizan Ali, R.
Ali Jan, A.
Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques
author_facet Azhar Ali, S.E.
Rizvi, S.S.H.
Lai, F.
Faizan Ali, R.
Ali Jan, A.
author_sort Azhar Ali, S.E.
title Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques
title_short Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques
title_full Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques
title_fullStr Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques
title_full_unstemmed Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques
title_sort predicting delinquency on mortgage loans: an exhaustive parametric comparison of machine learning techniques
publisher University of Novi Sad
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102691339&doi=10.24867%2fIJIEM-2021-1-272&partnerID=40&md5=6ad2d7eca1c90281cb5768c36e41c625
http://eprints.utp.edu.my/23739/
_version_ 1738656514798780416
score 13.211869

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