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<div><p>Electronic payment methods are increasingly prevalent worldwide, facilitating both in-person and online transactions. As credit card usage for online payments grows, fraud and payment defaults have also risen, resulting in significant financial losses. Detecting fraudulent transa...

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Bibliographic Details
Main Author:	Al Mahmud Siam (21728789) (author)
Other Authors:	Pankaj Bhowmik (6002078) (author), Md Palash Uddin (13473853) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified union operation combines time credit cards significant financial losses machine learning models increasingly prevalent worldwide highly imbalanced nature employing five state efficient feature selection decision support system credit card usage fraudulent activities constitute xgbc ), adaboost online payments grows et ), xgboost dataset &# 8216 random forest importance five diverse datasets detecting fraudulent transactions random forest fraudulent transactions online transactions world dataset rfi ), transformed datasets transaction datasets substantial contribution small fraction rfi evaluate results demonstrate remaining features proposed framework proposed approach practical solution pearson correlation payment defaults informative features information gain imbalance levels ig ), findings highlight ensuring comprehensive challenging due also risen also apply
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