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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM40005

Online Payment Fraud Detection by using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM40005

Volume:

8 |

12 |

Dec 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM40005

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MP

M. PRANITHA

BVK

B. VAMSI KRISHNA

LB

L. BHUMIKA

VJ

V. JAHNAVI

VJ

Vinoj J

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Abstract

Regarding the convenience of performing an installment from anywhere in the world, online installments have been a good quality. In the last decades, there has been an increment in online installments. E-installments enable businesses win a part of money in expansion to buyers. However, because electronic installments are very basic, there is also the danger of exploitation related with them. A buyer must ensure that the installment he is paying is going only to the right benefit supplier. Online fraud exposed people to the possibility of their information being compromised, as well as the hassle of having to report the fraud, block their installment strategy, and other things. When businesses are involved, it poses a few issues; sometimes, they have to issue discounts in order to keep customers. In this way, it becomes essential that both buyers and businesses be informed of such internet tricks. A show to check if an online payment is false or not is provided in this consider. To find out whether a specific Online payment is false or not, some features like the type of payment, the receiver’s character, etc. would be taken into consideration.

KEYWORDS

Onlinepayments E-payment Fraud Fraud Detection Consumer Protection Payment Security.

Cite This Article

M. PRANITHA, B. VAMSI KRISHNA, L. BHUMIKA, V. JAHNAVI, Vinoj J, "Online Payment Fraud Detection by using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 12, Dec 2024. https://doi.org/10.55041/IJSREM40005

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Publication Date

12/23/2024

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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3

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5

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This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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