Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques

One of the significant problems in the credit card fraud domain is the increasing number of imbalanced data. The higher ratio of majority to minority classes can lead to misleading results, as conventional machine learning algorithms assume equal class distribution. The first contribution of this re...

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Main Author:	Gasim, Esraa Faisal Malik
Format:	Thesis
Language:	English
Published:	2023
Subjects:	HG4001-4285 Finance management Business finance.Corporation finance
Online Access:	http://eprints.usm.my/60174/1/ESRAA%20FAISAL%20MALIK%20GASIM%20-%20TESIS%20cut.pdf
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id	my-usm-ep.60174
record_format	uketd_dc
spelling	my-usm-ep.601742024-03-13T07:17:21Z Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques 2023-07 Gasim, Esraa Faisal Malik HG4001-4285 Finance management. Business finance.Corporation finance One of the significant problems in the credit card fraud domain is the increasing number of imbalanced data. The higher ratio of majority to minority classes can lead to misleading results, as conventional machine learning algorithms assume equal class distribution. The first contribution of this research is to develop a new preprocessing technique that utilizes cost-sensitive learning and resampling techniques at the data-level to improve the performance of highly imbalanced datasets. The developed preprocessing technique consists of three phases. In the first phase, several resampling techniques at the data-level, such as SMOTE-ENN, SMOTE-TOMEK, SMOTE-OSS, SMOTE-RUS, and ROS-RUS with their default parameters, are compared to find the optimum technique with the highest performance. The second phase involves using cost-sensitive learning with different ratios to determine the best range of ratios to be used in phase three. Subsequently, in the third phase, the percentage of resampling techniques at the data-level is fine-tuned to avoid losing crucial information or producing repetitive synthetic data that could cause overfitting. Additionally, the cost-sensitive learning ratio is fine-tuned to determine the misclassification costs in the minority class. The developed new preprocessing technique was found to have a positive impact in terms of F1-measure and misclassification rate in contrast to the conventional resampling techniques. Furthermore, the negative effect of financial crimes on financial institutions has grown dramatically over the years. The second contribution to this research is to develop multiple hybrid machine learning models in order to enhance the detection of fraudulent activities in the credit card fraud detection domain. 2023-07 Thesis http://eprints.usm.my/60174/ http://eprints.usm.my/60174/1/ESRAA%20FAISAL%20MALIK%20GASIM%20-%20TESIS%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Pengurusan
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	HG4001-4285 Finance management Business finance.Corporation finance
spellingShingle	HG4001-4285 Finance management Business finance.Corporation finance Gasim, Esraa Faisal Malik Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques
description	One of the significant problems in the credit card fraud domain is the increasing number of imbalanced data. The higher ratio of majority to minority classes can lead to misleading results, as conventional machine learning algorithms assume equal class distribution. The first contribution of this research is to develop a new preprocessing technique that utilizes cost-sensitive learning and resampling techniques at the data-level to improve the performance of highly imbalanced datasets. The developed preprocessing technique consists of three phases. In the first phase, several resampling techniques at the data-level, such as SMOTE-ENN, SMOTE-TOMEK, SMOTE-OSS, SMOTE-RUS, and ROS-RUS with their default parameters, are compared to find the optimum technique with the highest performance. The second phase involves using cost-sensitive learning with different ratios to determine the best range of ratios to be used in phase three. Subsequently, in the third phase, the percentage of resampling techniques at the data-level is fine-tuned to avoid losing crucial information or producing repetitive synthetic data that could cause overfitting. Additionally, the cost-sensitive learning ratio is fine-tuned to determine the misclassification costs in the minority class. The developed new preprocessing technique was found to have a positive impact in terms of F1-measure and misclassification rate in contrast to the conventional resampling techniques. Furthermore, the negative effect of financial crimes on financial institutions has grown dramatically over the years. The second contribution to this research is to develop multiple hybrid machine learning models in order to enhance the detection of fraudulent activities in the credit card fraud detection domain.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Gasim, Esraa Faisal Malik
author_facet	Gasim, Esraa Faisal Malik
author_sort	Gasim, Esraa Faisal Malik
title	Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques
title_short	Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques
title_full	Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques
title_fullStr	Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques
title_full_unstemmed	Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques
title_sort	credit card fraud detection using new preprocessing and hybrid machine learning techniques
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Pengurusan
publishDate	2023
url	http://eprints.usm.my/60174/1/ESRAA%20FAISAL%20MALIK%20GASIM%20-%20TESIS%20cut.pdf
_version_	1794024099968712704

Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques

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