An Analysis on Fraud Detection in Credit Card Transactions using Machine Learning Techniques

Mathew, Jincy C; Nithya, B.; Vishwanatha, C R; Shetty, Prathiksha; Priya, H.; Kavya, G

doi:10.1109/icais53314.2022.9742830

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…Additionally, meth-ods for addressing false negatives and achieving real-world effectiveness are explored. This study underscores the potential of the proposed methods in effec-tively countering credit card fraud, supported by empirical evidence [10].…”

Section: Neural Networksupporting

confidence: 68%

“…The proposed method involves utilizing adaptive boosting (AdaBoost) in conjunction with a long short-term memory (LSTM) neural network as the foundation for the ensemble classifier. This study, as outlined in [10], presents a valuable contribution to credit card fraud detection, leveraging advanced tech-niques to tackle the complexities associated with evolving purchase patterns and biased datasets…”

Section: Neural Networkmentioning

confidence: 99%

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Credit Card Fraud Detection Methods: A Review

Pundkar,

Zubei

2023

E3S Web Conf.

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In today’s context, the term “fraud” has become closely intertwined with credit card-related deceit. Recent years have witnessed a notable surge in both credit card utilization and fraudulent activities. Detecting and thwarting fraud necessitates a meticulous analysis of customers’ spending patterns. The ubiquity of credit card use for both online and in-store transactions has un-fortunately led to a parallel rise in recognition valentine scam occurrences. While the primary area of deception discovery is the documentation of sham incidents, the urgency of promptly flagging such events cannot be overstated. The con-temporary landscape heavily favors credit card usage, a trend that inadvert-ently contributes to the annual expansion of fraudulent gains. This unlawful practice exerts a pernicious influence on the global economy at large, exac-erbating its impact year after year. Numerous cutting-edge methods, including as data mining, machine learning, fuzzy logic, genetic programming, sequence alignment, artificial intelligence, and fuzzy logic, have become indispensable in the fight against this threat when it comes to identifying credit card fraud. This study delves into the intricate integration of data mining methodologies, showcas-ing their robust potential to provide comprehensive coverage against fraudu-lent activities while maintaining a controlled balance between false alarms and detection accuracy. Within the financial sector, the challenge of credit card fraud detection remains both persistent and pressing. This paper intro-duces an innovative paradigm aimed at fortifying credit card fraud detection. This is achieved by synergistically harnessing the capabilities of the Artificial Underground Over-sampling Practice (SMOTE), the potency of Adaptive Increasing (ADABoost), and the privacy-enhancing attributes of Federated Learning. The incorporation of federated learning serves a dual purpose: not only does it address prevailing data privacy concerns, but it also significantly augments the precision of fraud detection across a diverse array of geograph-ically distributed data sources

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Section: Neural Networksupporting

confidence: 68%

Section: Neural Networkmentioning

confidence: 99%

Credit Card Fraud Detection Methods: A Review

Pundkar,

Zubei

2023

E3S Web Conf.

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“…What is more, these researchers develop an open-source fake-shop detection API and middleware that enable risk assessment of any website. The second article [66] proposed a system for detecting e-commerce websites that is based on Statistical Learning Theory (SLT). The researchers conduct a series of experiments, comparing their proposed solution with current methods on a test data set containing 900 websites.…”

Section: Triangulation Fraudmentioning

confidence: 99%

“…The researchers surface key behavior patterns of bots that include less spatial motion as detected by device sensors (1/10 of human users), a higher IP clustering ratio (60 percent in bots vs. 15 percent in human users), a higher jailbroken device rate (92 percent in bots vs. 4 percent in human users), more irregular device names, and fewer IP address changes in bots. The final article in the category [66] looks at this issue of cloud bots and how they can be used to perform click fraud, register fake accounts, and commit other types of fraud. The researchers proposed a traffic-based quasireal-time method for cloud bot detection using machine learning that exploits a new sample partitioning approach as well as innovative multi-layer features that reveal the essential difference between bots and human traffic.…”

Section: Bot 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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