Effective fraud detection in e-commerce: Leveraging machine learning and big data analytics

被引：0

作者：

Byrapu Reddy, Surendranadha Reddy ^{[1
]}

Kanagala, Praneeth ^{[2
]}

Ravichandran, Prabu ^{[3
]}

Pulimamidi, Dr Rahul ^{[4
]}

Sivarambabu, P.V. ^{[5
]}

Polireddi, Naga Simhadri Apparao ^{[6
]}

机构：

[1] Analyst Information Technology Northeastern University Lincoln Financial Group Atlanta, GA

[2] Information Technology, University of the Cumberlands, Williamsburg, KY

[3] Data Architect, AWS, Amazon, Raleigh, NC

[4] UI Architect/Software Developer, Laboratory Corporation of America Holdings, Durham, NC

[5] Department of CSE, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Andhra Pradesh, Guntur

[6] Senior Software Engineer IKON Tech Services LLC Phoenix, AZ

来源：

Measurement: Sensors | 2024年 / 33卷

关键词：

Big data; E-commerce; Fraud detection; Machine learning; Online transaction;

D O I：

10.1016/j.measen.2024.101138

中图分类号：

学科分类号：

摘要：

Sophisticated cyber-infrastructure and information technology methods are necessary to exploit and analyse the massive amounts of data generated by online transactions. This study introduces a big data platform for online retailers to tackle various issues in the e-commerce industry. Both people and businesses are vulnerable to fraud, which is a worldwide problem. In today's tech-driven society, the battle against fraud has been greatly aided by machine learning (ML) and artificial intelligence (AI). This essay takes a look at the conventional wisdom about fraud prevention and shows how outdated it is when compared to modern fraud techniques. It delves further into the ways in which ML and AI are supporting fast digitization, which in turn revolutionises fraud prevention efforts. Machine learning and artificial intelligence algorithms enable companies to comb through massive amounts of data for patterns and anomalies that could suggest fraudulent activity. In this article, we will explore how machine learning and artificial intelligence may greatly enhance fraud prevention efforts. These technologies can help with advanced data analytics, anomaly detection, and predictive modelling. The text highlights the ways in which these technologies empower organisations to proactively identify and reduce fraud risks, protecting both their operations and stakeholders. © 2024 The Authors

引用

共 19 条

[1]

Hilal W., Gadsden S.A., Yawney J., Financial fraud: a review of anomaly detection techniques and recent advances, Expert Syst. Appl., 193, (2021)

[2]

Ashtiani M.N., Raahemi B., Intelligent fraud detection in financial statements using machine learning and data mining: a systematic literature review, IEEE Access, 10, pp. 72504-72525, (2021)

[3]

Albashrawi M., Detecting financial fraud using data mining techniques: a decade review from 2004 to 2015, J. Data Sci., 14, pp. 553-570, (2016)

[4]

Choi D., Lee K., An artificial intelligence approach to financial fraud detection under IoT environment: a survey and implementation, Secur. Commun. Network., 2018, pp. 1-15, (2018)

[5]

Ngai E.W.T., Hu Y., Wong Y.H., Chen Y., Sun X., The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature, Decis. Support Syst., 50, pp. 559-569, (2011)

[6]

Ryman-Tubb N.F., Krause P., Garn W., How Artificial Intelligence and machine learning research impacts payment card fraud detection: a survey and industry benchmark, Eng. Appl. Artif. Intell., 76, pp. 130-157, (2018)

[7]

Al-Hashedi K.G., Magalingam P., Financial fraud detection applying data mining techniques: a comprehensive review from 2009 to 2019, Comput. Sci. Rev., 40, (2021)

[8]

Chaquet-ulldemolins J., Moral-rubio S., Munoz-romero S., On the black-box challenge for fraud detection using machine learning (II): nonlinear analysis through interpretable autoencoders, Appl. Sci., 12, (2022)

[9]

Da'U A., Salim N., Recommendation system based on deep learning methods: a systematic review and new directions, Artif. Intell. Rev., 53, pp. 2709-2748, (2019)

[10]

Zeng Y., Tang J., RLC-GNN: an improved deep architecture for spatial-based graph neural network with application to fraud detection, Appl. Sci., 11, (2021)

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