RNN-BD: an approach for fraud visualisation and detection using deep learning

Rao, G. Madhukar; Srinivas, K.

doi:10.1504/ijcse.2022.122212

Cited by 3 publications

(2 citation statements)

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“…The last decade has seen significant advances in Natural Language Processing (NLP) and computer vision techniques that can do well in creating multiple learning contexts to build detection systems that are robust to the high level of dynamism observed in various fraud domains. A few articles in our corpus incorporate text-based methods [47,95,104,106] in their detection models, but there are no articles looking at multi-modal approaches incorporating image data into training. This is a future research opportunity area for this domain.…”

Section: Training Datamentioning

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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Section: Training Datamentioning

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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“…Instead of grooming a given dataset to run through predefined algorithms, DL can set up parameters about the dataset and train the computational system to learn on its own by recognizing patterns using many layers of processing. There are various variants of DL models, which include but are not limited to, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), adversarial Neural Networks (NN), and deep autoencoders [22][23][24][25] each of which are frequently applied to the OCCF detection research domain.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis of Recent Trends in Machine Learning for Online Credit Card Fraud Detection

Hove,

Olugbara,

Singh

2024

J Scientometric Res.

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Online credit card fraud (OCCF) is the malicious act of using credit card details belonging to another person to complete fraudulent transactions over the Internet. Naturally, masses of researchers have engaged in the imperative search for effective solutions across a wide range of disciplines. The result is a rich tapestry of methodologies, models, frameworks, and inventions exhibiting dramatic spread and growth. However, this also results in an unorganized research domain. In this state, a bibliometric analysis is a useful technique for establishing a reconciled snapshot of the OCCF research domain. This paper has particular interest in determining the intellectual structure of the knowledge of machine learning, deep learning, and ensemble learning models for early detection of OCCF. This bibliometric analysis is conducted using 524 publications between 2013 and 2022 extracted from the SCOPUS core collection database. Microsoft Excel, VOSViewer, and Biblioshiny software tools were used for data analysis. The findings indicate that ensemble learning models are trending and the three most authoritative authors have been exposed in this study. There is a sharp rise in global publications annually and India has the most publications with the most impactful authors. Five broad clusters of knowledge are imbalanced data, anomaly detection, machine learning, decision trees, and ensemble learning. Intellectual collaboration across regions is strong amongst Asia, Europe, and North America with weak associations between Africa and South America. This is the first bibliometric analysis in the domain of OCCF detection to the best of the author's ability. The findings significantly contribute to the application of OCCF detection through the creation of intellectual patterns in existing literature. The results bring about synthesis within a domain of research that is currently disorganized. This in turn helps researchers to identify research gaps, and areas for further research and formulate a curriculum.

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