A Multi-perspective Fraud Detection Method for Multi- Participant E-commerce Transaction

Registration ID: IJNRD_217329

Published ID: IJNRD2404218

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A.Likhitha, Y.Pallavi, B.Iswarya, J.Madhuri, R.Naveen kumar

Multiparticipant E-commerce Transactions, Fraud Detection, User Behaviors, Abnormalities Analysis, Ensemble Classification Model, Random Forest, Gradient Boosting, AdaBoost

In the realm of e-commerce, where transactions involve multiple participants such as buyers, sellers, and intermediaries, the detection of fraudulent activities presents a significant challenge. To address this issue, our proposed method focuses on a Multi perspective approach aimed at enhancing fraud detection accuracy and efficiency. The first step involves the detection of user behaviors, wherein we leverage various techniques such as behavioral analysis and examination of transaction histories to gain insights into normal user behavior patterns. By understanding typical user interactions within the e-commerce ecosystem, we establish a baseline against which abnormal behaviors can be identified. Subsequently, we delve into the analysis of abnormalities for feature extraction. Utilizing sophisticated anomaly detection algorithms, we scrutinize transaction data to uncover irregular patterns indicative of potentially fraudulent activities. This process allows us to extract important features that serve as key indicators for fraud detection. Finally, we employ an ensemble classification model to implement our fraud detection mechanism, avoiding reliance on a specific algorithm. Instead, we leverage the strengths of ensemble algorithms, such as Random Forest, Gradient Boosting, or Ada Boost. By feeding the extracted features into the ensemble model, we train it to discern between legitimate and fraudulent behaviors in multi-participant e-commerce transactions. Ensemble methods are particularly well-suited for this task due to their ability to handle high-dimensional data and capture complex decision boundaries through the combination of diverse base models.

Published Paper   E-Certificate

A.Likhitha, Y.Pallavi, B.Iswarya, J.Madhuri, & R.Naveen kumar (April-2024). A Multi-perspective Fraud Detection Method for Multi- Participant E-commerce Transaction. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), b959-b963. https://ijnrd.org/papers/IJNRD2404218.pdf

Volume 9 Issue 4, April-2024

Pages : b959-b963

Paper Reg. ID: IJNRD_217329

Published Paper Id: IJNRD2404218

Downloads: 000122254

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Ananthapur, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404218.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404218

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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