Zobrazeno 1 - 10
of 396
pro vyhledávání: '"Credit Card Fraud Detection"'
Autor:
rawaa ismael
Publikováno v:
Iraqi Journal for Computers and Informatics, Vol 50, Iss 1, Pp 1-7 (2024)
Over time, the growth of credit cards and the financial data need credit models to support banks in making financial decisions. So, to avoid fraud in internet transactions which increased with the growth of technology it is crucial to develop an effi
Externí odkaz:
https://doaj.org/article/9861724c36a94173950ed3622ca9119a
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-22 (2024)
Abstract Acquiring labeled datasets often incurs substantial costs primarily due to the requirement of expert human intervention to produce accurate and reliable class labels. In the modern data landscape, an overwhelming proportion of newly generate
Externí odkaz:
https://doaj.org/article/977d85c21b024c07afbcf3f61c5af59b
Publikováno v:
IEEE Access, Vol 12, Pp 159316-159335 (2024)
Credit card fraud (CCF) is a significant threat to cardholders and financial institutions. CCF detection against this threat is challenging due to extremely imbalanced data (EID). EID involves extremely few instances of fraud for training and an extr
Externí odkaz:
https://doaj.org/article/2e03618967114c458023b8752332785f
Publikováno v:
IEEE Access, Vol 12, Pp 157006-157020 (2024)
Credit card fraud detection remains a significant challenge in the financial industry, necessitating advanced models to identify fraudulent activities while minimizing false positives accurately. Traditional machine learning approaches, such as Multi
Externí odkaz:
https://doaj.org/article/5aa867a2bae94f3cbc143a5a8f7bc383
Autor:
Emmanuel Ileberi, Yanxia Sun
Publikováno v:
IEEE Access, Vol 12, Pp 133315-133327 (2024)
Online card transactions have become more frequent due to the growth of e-commerce and financial technology apps. However, this also means more opportunities for credit card fraud, which affects banks, retailers, and card issuers. Therefore, we need
Externí odkaz:
https://doaj.org/article/22cc2964f20043bc9c61a3a47ff3cf9a
Publikováno v:
IEEE Access, Vol 12, Pp 132421-132433 (2024)
In recent times, credit card fraud has emerged as a substantial financial challenge for both cardholders and the issuing authorities. To address this demanding issue, researchers have employed machine learning techniques to identify fraudulent activi
Externí odkaz:
https://doaj.org/article/1798416eb95c42e8b9216cbde2e98d24
Publikováno v:
IEEE Access, Vol 12, Pp 54893-54900 (2024)
Recognizing fraudulent credit card transactions is one of the main issues facing banking institutions. Since each transaction that completes the authentication procedure must be authorized by financial institutions, a hacker might pose as the actual
Externí odkaz:
https://doaj.org/article/43472a0263984127a9784eaa5c02275b
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 3, Pp 102003- (2024)
Credit card fraud is a significant problem, with millions of dollars lost each year. Detecting fraudulent transactions is a challenging task due to the large volume of data and the constantly evolving tactics of fraudsters. Likewise any detection pro
Externí odkaz:
https://doaj.org/article/6850627d1832436e977338ea89e7a5bc
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-22 (2023)
Abstract Output thresholding is well-suited for addressing class imbalance, since the technique does not increase dataset size, run the risk of discarding important instances, or modify an existing learner. Through the use of the Credit Card Fraud De
Externí odkaz:
https://doaj.org/article/f8470606c8074b5ab96cacc7fd4e2643
Autor:
Xiaoyan Zhao, Shaopeng Guan
Publikováno v:
PeerJ Computer Science, Vol 9, p e1634 (2023)
Credit card fraud can lead to significant financial losses for both individuals and financial institutions. In this article, we propose a novel method called CTCN, which uses Conditional Tabular Generative Adversarial Networks (CTGAN) and temporal co
Externí odkaz:
https://doaj.org/article/bae209b6103244f98508e60bcb0dbc7a