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Published Paper Details:
A Machine Learning Approach to Credit Card Fraud Detection Using Logistic Regression and Random Forest
Authors
Sara Tesema,Eden Selemon,Dagim Dagmawi,Kemal Mohammed,Mr. Diriba Giichile
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Keywords
CCFD = Credit Card Fraud Detection ML = Machine Learning
Abstract
ABSTRACT: Scientific progress in electronic commerce and communication networks led to
credit cards becoming the most popular payment instrument supporting in-store and electronic
purchasing transactions. The number of credit card payment fraud incidents has substantially
escalated because of current transaction trends. The yearly financial losses from fraud damages
credit card issuers to such an extent that they need an advanced system with a complete fraud
detection framework. Electronic payments have become a leading factor that drives the increase
in fraudulent transactions. The urgent need exists for new methods which can detect credit card
transaction fraud before it happens. Our fundamental research develops a transaction fraud
system which integrates machine learning elements with original feature modification strategies.
Our method received validation through experiments conducted with two real-world public
credit card transaction datasets where one of them contained actual fraud cases. Our system
achieved the highest success rate compared to other competing methods when processing both
datasets. Our proposed method proves highly effective based on the achieved results. The tool
from our approach strengthens credit card issuer capabilities to recognize fraudulent transactions
thus safeguarding their customers while simultaneously minimizing loss and regulatory expense.
A protected transaction environment for credit cards can emerge from implementing the
procedures we have recommended.
IJCRT's Publication Details
Unique Identification Number - IJCRT25A4504
Paper ID - 283166
Author type - Indian Author
Page Number(s) - m828-m835
Pubished in - Volume 13 | Issue 4 | April 2025
DOI (Digital Object Identifier) -
No Of Downloads - 87
Author Country - India, 110042, New Delhi, New Delhi, 110042, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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Sara Tesema,Eden Selemon,Dagim Dagmawi,Kemal Mohammed,Mr. Diriba Giichile, "A Machine Learning Approach to Credit Card Fraud Detection Using Logistic Regression and Random Forest", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 4, pp.m828-m835, April 2025, Available at :http://www.ijcrt.org/papers/IJCRT25A4504.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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