INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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.
Keywords:
Multiparticipant E-commerce Transactions, Fraud Detection, User Behaviors, Abnormalities Analysis, Ensemble Classification Model, Random Forest, Gradient Boosting, AdaBoost
Cite Article:
"A Multi-perspective Fraud Detection Method for Multi- Participant E-commerce Transaction", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b959-b963, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404218.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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