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Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection

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Last updated: 03 Jan 2025

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Abstract

Online payment fraud detection is crucial for safeguarding e-commerce transactions against sophisticated fraudsters who exploit system vulnerabilities. This paper proposes an efficient framework for predicting online payment fraud, employing six diverse machine learning algorithms, namely constant, CN7Rule induction, KNN, Tree, Random Forest, Gradient boosting, SVM, Logistic regression, Naive Bayes, Ada boost, Neural network, and stochastic gradient descent, on three distinct datasets. The gradient-boosting algorithm consistently outperformed others through rigorous testing, achieving an impressive accuracy rate of 99.7%. This algorithm demonstrated resilience across various testing scenarios, establishing itself as the most effective online payment fraud detection solution. With the highest accuracy score of 99.7% in all testing phases, gradient boosting is optimal for preemptive measures against fraudulent activities in electronic transactions, providing a robust defense mechanism for e-commerce platforms.

DOI

10.21608/jocc.2024.339929

Keywords

Online payment fraud, machine-learning, Gradient boosting, CN2Rule Induction, fraud deduction

Authors

First Name

Maged

Last Name

Farouk

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

Email

melsayed@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Nashwa

Last Name

Shaker

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

Email

nragab@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Diaa

Last Name

AbdElminaam

MiddleName

s

Affiliation

Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt

Email

diaa.salama@miuegypt.edu.eg

City

-

Orcid

0000-0002-1544-9906

First Name

Omnia

Last Name

Elrashidy

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

Email

oelrashidy@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Nada

Last Name

Ghorab

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein University, Alamein, Egypt

Email

nada.ghorab.2023@aiu.edu.eg

City

-

Orcid

-

First Name

Jevana

Last Name

Hany

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein University, Alamein, Egypt

Email

jevana.mikhael.2023@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Alaa

Last Name

Amr

MiddleName

-

Affiliation

aDepartment of Business Information Systems, Faculty of Business, Alamein University, Alamein, Egypt

Email

alaa.ibrahim.2023@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Omar

Last Name

Adel

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein University, Alamein, Egypt

Email

omar.aboelnaga.2023@aiu.edu.eg

City

-

Orcid

-

First Name

Kriols

Last Name

Saad

MiddleName

-

Affiliation

aDepartment of Business Information Systems, Faculty of Business, Alamein University, Alamein, Egypt

Email

kirols.wahba.2023@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Khaled

Last Name

Ali

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein University, Alamein, Egypt

Email

khaled.elmango.2023@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Reda

Last Name

Elazab

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

Email

relazab@aiu.edu.eg

City

Alamein

Orcid

-

Volume

3

Article Issue

1

Related Issue

45956

Issue Date

2024-01-01

Receive Date

2024-01-08

Publish Date

2024-02-04

Page Start

116

Page End

131

Online ISSN

2636-3577

Article File

JOCC_Volume 3_Issue 1_Pages 116-131.pdf

PDF . 1.9MB

Link

https://jocc.journals.ekb.eg/article_339929.html

Detail API

https://jocc.journals.ekb.eg/service?article_code=339929

Order

9

Type

Original Article

Type Code

731

Publication Type

Journal

Publication Title

Journal of Computing and Communication

Publication Link

https://jocc.journals.ekb.eg/

MainTitle

Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection

Details

Type

Article

Created At

24 Dec 2024