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Experimental Machine Learning and Deep Learning Credit Card Fraud Detection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Experimental Machine Learning and Deep Learning Credit Card Fraud Detection./
Author:	Balmakhtar, Marouane.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	152 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Information technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28644281
ISBN:	9798535516417

Experimental Machine Learning and Deep Learning Credit Card Fraud Detection.
Balmakhtar, Marouane.

Experimental Machine Learning and Deep Learning Credit Card Fraud Detection. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 152 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Indiana University of Pennsylvania, 2021.

This item must not be sold to any third party vendors.

The importance of developing credit card fraud detection systems has long been recognized by finance and accounting communities. However, businesses are still facing credit card fraud incidents that continue to explode due to the growing number of online business transactions in the new digital transformation era. Cybersecurity data breaches are on the rise, which can render existing credit card fraud detection systems ineffective at identifying advanced fraudulent activities. The credit card fraud detection problem appears to be especially challenging from a cybersecurity perspective because of (1) the lack of enriched datasets and (2) class imbalance. Credit card fraud has developed into an important concern in cyberspace. Various techniques are utilized in addressing these credit card fraud threats, but now there is the need more than ever to employ experimental methods that include a mixture of machine and deep learning methods to detect fraudulent activities and factor analysis to try to make sense of the interrelationships amongst the features in a typical anonymized credit card data to glean any insights that can help address credit card fraud. This research evaluates various machine learning methods commonly used to address credit card fraud problems and cybersecurity issues that are binary in nature. This research introduces a new method by combining Extreme Boosting Gradient (EGB) and Deep Neural Network (DNN) that can be implemented in a credit card fraud detection system. Experiments show that (a) EGB performs the best in detecting credit card fraud amongst other individual models, and (b) the hybrid model (EGB + DNN) outperforms DNN alone. This research also proposes a conceptual framework based on an anonymized real-world dataset.

ISBN: 9798535516417Subjects--Topical Terms:

532993
Information technology.
Subjects--Index Terms:

Machine learning

Experimental Machine Learning and Deep Learning Credit Card Fraud Detection.
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300 $a 152 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Paz, Veronica.
502 $a Thesis (Ph.D.)--Indiana University of Pennsylvania, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The importance of developing credit card fraud detection systems has long been recognized by finance and accounting communities. However, businesses are still facing credit card fraud incidents that continue to explode due to the growing number of online business transactions in the new digital transformation era. Cybersecurity data breaches are on the rise, which can render existing credit card fraud detection systems ineffective at identifying advanced fraudulent activities. The credit card fraud detection problem appears to be especially challenging from a cybersecurity perspective because of (1) the lack of enriched datasets and (2) class imbalance. Credit card fraud has developed into an important concern in cyberspace. Various techniques are utilized in addressing these credit card fraud threats, but now there is the need more than ever to employ experimental methods that include a mixture of machine and deep learning methods to detect fraudulent activities and factor analysis to try to make sense of the interrelationships amongst the features in a typical anonymized credit card data to glean any insights that can help address credit card fraud. This research evaluates various machine learning methods commonly used to address credit card fraud problems and cybersecurity issues that are binary in nature. This research introduces a new method by combining Extreme Boosting Gradient (EGB) and Deep Neural Network (DNN) that can be implemented in a credit card fraud detection system. Experiments show that (a) EGB performs the best in detecting credit card fraud amongst other individual models, and (b) the hybrid model (EGB + DNN) outperforms DNN alone. This research also proposes a conceptual framework based on an anonymized real-world dataset.
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650 4 $a Internet crime. $3 3541387
650 4 $a Identity theft. $3 1569054
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