Credit Card Fraud Detection using Apache Spark Analysis

In the current digital era, data is budding tremendously from various sources like banks, businesses, education, entertainment, etc. Due to its significant consequence, it became a prominent proceeding for numerous research areas like the semantic web, machine learning, computational intelligence, and data mining. For knowledge extraction, several corporate sectors depend on tweets, blogs, and social data to get adequate analysis. It helps them predict the customer’s tastes and preferences, optimize the usage of resources. In some cases, the same data creates complications that lead to a problem named as big data. To solve this, so many researchers have given various solutions. Based on literature analysis formulated 6-s simulation towards big data, detailed information about characteristics, a taxonomy of tools, and discussed various processing paradigms. No one tool can truly fit for all solutions, so this paper helps to make decisions smoothly by providing enough information and discussing major privacy issues and future directions.

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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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Credit Card Fraud Detection using Apache Spark Analysis

Cited by 4 publications

References 4 publications

Examining Amazon Customer Reviews using PySpark and AWS: A Data Lake Approach

Examining Amazon Customer Reviews using PySpark and AWS: A Data Lake Approach

Big Data Analytics, Processing Models, Taxonomy of Tools, V’s, and Challenges: State-of-Art Review and Future Implications

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

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