Development of customer response prediction model for personal loan using logistic regression

In general, financial service companies use mass media marketing strategies to offer and promote a product or service to their customers. This strategy is seen as less effective because differences in customer interests are not taken into account. With a large customer database, financial service co...

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Main Author:	Nur Hafizah, Zakaria
Format:	Thesis
Language:	eng eng
Published:	2023
Subjects:	HG Finance
Online Access:	https://etd.uum.edu.my/10906/2/depositpermission_817737.pdf https://etd.uum.edu.my/10906/3/s817737_01.pdf
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id	my-uum-etd.10906
record_format	uketd_dc
spelling	my-uum-etd.109062024-01-18T06:25:04Z Development of customer response prediction model for personal loan using logistic regression 2023 Nur Hafizah, Zakaria Mohd Shaharanee, Izwan Nizal Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences HG Finance In general, financial service companies use mass media marketing strategies to offer and promote a product or service to their customers. This strategy is seen as less effective because differences in customer interests are not taken into account. With a large customer database, financial service companies have shifted from unproductive mass media marketing strategies to targeted marketing strategies by identifying customers who are likely to respond to more specific products and services. However, this strategy appears challenging when implemented manually through a large customer database. Therefore, data mining techniques are used to develop accurate customer response forecasting models. The goal of this study is to develop a customer response model to predict whether a customer will respond to a personal loan product using logistic regression and decision tree techniques. For modelling purposes, 10,952 customer data were collected from one commercial bank in Malaysia. Factor analysis was conducted to identify inputs for the model. Finally, logistic regression and decision tree techniques were used to develop the customer response model. Different models from these two techniques were compared. The best model for predicting customers who are likely to respond to personal loan products was selected based on the highest accuracy, precision, and recall values. Both models had the same accuracy of around 98%. However, decision tree performed slightly better than regression, especially for 10-fold cross-validation. For 5-fold cross-validation, logistic regression performed well. In most cases, decision trees should perform better in business scenarios where logistic regression performs better when data contamination is low. This study can benefit banks by maximizing customer response to product offers, minimizing overall market costs, and improving customer relationship management 2023 Thesis https://etd.uum.edu.my/10906/ https://etd.uum.edu.my/10906/2/depositpermission_817737.pdf text eng staffonly https://etd.uum.edu.my/10906/3/s817737_01.pdf text eng public other masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mohd Shaharanee, Izwan Nizal
topic	HG Finance
spellingShingle	HG Finance Nur Hafizah, Zakaria Development of customer response prediction model for personal loan using logistic regression
description	In general, financial service companies use mass media marketing strategies to offer and promote a product or service to their customers. This strategy is seen as less effective because differences in customer interests are not taken into account. With a large customer database, financial service companies have shifted from unproductive mass media marketing strategies to targeted marketing strategies by identifying customers who are likely to respond to more specific products and services. However, this strategy appears challenging when implemented manually through a large customer database. Therefore, data mining techniques are used to develop accurate customer response forecasting models. The goal of this study is to develop a customer response model to predict whether a customer will respond to a personal loan product using logistic regression and decision tree techniques. For modelling purposes, 10,952 customer data were collected from one commercial bank in Malaysia. Factor analysis was conducted to identify inputs for the model. Finally, logistic regression and decision tree techniques were used to develop the customer response model. Different models from these two techniques were compared. The best model for predicting customers who are likely to respond to personal loan products was selected based on the highest accuracy, precision, and recall values. Both models had the same accuracy of around 98%. However, decision tree performed slightly better than regression, especially for 10-fold cross-validation. For 5-fold cross-validation, logistic regression performed well. In most cases, decision trees should perform better in business scenarios where logistic regression performs better when data contamination is low. This study can benefit banks by maximizing customer response to product offers, minimizing overall market costs, and improving customer relationship management
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Nur Hafizah, Zakaria
author_facet	Nur Hafizah, Zakaria
author_sort	Nur Hafizah, Zakaria
title	Development of customer response prediction model for personal loan using logistic regression
title_short	Development of customer response prediction model for personal loan using logistic regression
title_full	Development of customer response prediction model for personal loan using logistic regression
title_fullStr	Development of customer response prediction model for personal loan using logistic regression
title_full_unstemmed	Development of customer response prediction model for personal loan using logistic regression
title_sort	development of customer response prediction model for personal loan using logistic regression
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2023
url	https://etd.uum.edu.my/10906/2/depositpermission_817737.pdf https://etd.uum.edu.my/10906/3/s817737_01.pdf
_version_	1794023786334388224

Development of customer response prediction model for personal loan using logistic regression

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