A Proposed Fraud Detection Model based on e-Payments Attributes a Case Study in Egyptian e-Payment Gateway

Nasr, Mohamed Hassan; Farrag, Mohamed Hassan; Nasr, Mona

doi:10.14569/ijacsa.2022.0130522

Cited by 5 publications

(5 citation statements)

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“…The other articles in this category include Refs. [45,63,64], who use a combination of data mining and machine learning methods to detect phishing fraud.…”

Section: Spam/phishing Fraudmentioning

confidence: 99%

E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review

Mutemi,

Bacao

2024

Big Data Min. Anal.

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The e-commerce industry's rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.

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“…The other articles in this category include Refs. [45,63,64], who use a combination of data mining and machine learning methods to detect phishing fraud.…”

Section: Spam/phishing Fraudmentioning

confidence: 99%

E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review

Mutemi,

Bacao

2024

Big Data Min. Anal.

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“…In [5], the authors explained that the model resulted in a considerable reduction in fraud and savings of 101,970.52 EGP out of 131,297.83 EGP. It was constructed using the IBM SPSS modeler's decision tree.…”

Section: Related Workmentioning

confidence: 99%

Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection

Farouk,

Shaker,

AbdElminaam

et al. 2024

Journal of Computing and Communication

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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.

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“…Nasr, Mona; Shaaban, Essam; Samir, Ahmed in 2019 [13] mentioned that management takes an interest in employees' work to guarantee that the appropriate person is put in the right position at the correct time. Since then, data mining (DM) has played an increasingly important part in the process of gaining knowledge from vast volumes of data.…”

Section: Related Workmentioning

confidence: 99%

“…Among the techniques are Decision Trees (DT), Nave Bayes, and Support Vector Machines (SVM). With the best prediction accuracy across all three studies, the SVM approach emerged as the most successful classifier for creating the pre-Journal of Software Engineering and Applications dictive model [13].…”

Section: Related Workmentioning

confidence: 99%

An Improved Enterprise Resource Planning System Using Machine Learning Techniques

Zakaria,

Abdelbadea,

Raslan

et al. 2024

JSEA

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Traditional Enterprise Resource Planning (ERP) systems with relational databases take weeks to deliver predictable insights instantly. The most accurate information is provided to companies to make the best decisions through advanced analytics that examine the past and the future and capture information about the present. Integrating machine learning (ML) into financial ERP systems offers several benefits, including increased accuracy, efficiency, and cost savings. Also, ERP systems are crucial in overseeing different aspects of Human Capital Management (HCM) in organizations. The performance of the staff draws the interest of the management. In particular, to guarantee that the proper employees are assigned to the convenient task at the suitable moment, train and qualify them, and build evaluation systems to follow up their performance and an attempt to maintain the potential talents of workers. Also, predicting employee salaries correctly is necessary for the efficient distribution of resources, retaining talent, and ensuring the success of the organization as a whole. Conventional ERP system salary forecasting methods typically use static reports that only show the system's current state, without analyzing employee data or providing recommendations. We designed and enforced a prototype to define to apply ML algorithms on Oracle EBS data to enhance employee evaluation using real-time data directly from the ERP system. Based on measurements of accuracy, the Random Forest algorithm enhanced the performance of this system. This model offers an accuracy of 90% on the balanced dataset.

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A Proposed Fraud Detection Model based on e-Payments Attributes a Case Study in Egyptian e-Payment Gateway

Cited by 5 publications

References 0 publications

E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review

E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review

Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection

An Improved Enterprise Resource Planning System Using Machine Learning Techniques

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