Authors :
Shakuntala D. S.; Shreyansh Kuchanur; Smitha M. P.; Sumanth J. M.; Dr. Kavitha C.
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/2m8ywk82
Scribd :
https://tinyurl.com/mr32bb3k
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR961
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
While credit card fraud and abuse are the way
becoming more common, the convenience using of there
credit for the way online purchases has also improved.
These fraudulent activities pose a severe financial danger to
both credit the are card users and way they using it financial
institutions. The first thinking aims to do of this research
project is to recognize and put an end to these kinds of
fraudulent activities. It addresses a broad range of subjects,
including the frequency of false positives, imbalanced
datasets, evolving fraud trends, and restricted public data
access.
The literature now under publication offers a range of
machine learning-based methods, including logistic
regression, decision trees, random the forests, support to the
vector of the +machines, and XG Boost, with the purpose of
identifying credit card fraud. However, these methods often
exhibit lower accuracy rates, highlighting the need for more
advanced deep learning algorithms in order to effectively
lower fraud losses. Therefore, the of way they primary
thinks objective of this is to improve fraud detection
abilities through which the way this application most be of
state- of-the-art deep learning algorithms.
An assessment way it should be of the research project
reveals better outcomes, including optimized AUC curves,
precision, f1-score, and accuracy.
The ultimate objective there are is to create models
that will greatly improve credit card fraud detection and
prevention. This research focuses on advanced deep
learning techniques in to the way it is order they provide the
to more reliable and accurate fraud detection mechanisms,
enhance security for credit card users, and lower financial
risks for financial institutions when conducting online
transactions.
Keywords :
Transaction Data Analytics, Online Fraud, Credit Card Fraud, Deep Learning, Machine Learning, Fraud Detection.
While credit card fraud and abuse are the way
becoming more common, the convenience using of there
credit for the way online purchases has also improved.
These fraudulent activities pose a severe financial danger to
both credit the are card users and way they using it financial
institutions. The first thinking aims to do of this research
project is to recognize and put an end to these kinds of
fraudulent activities. It addresses a broad range of subjects,
including the frequency of false positives, imbalanced
datasets, evolving fraud trends, and restricted public data
access.
The literature now under publication offers a range of
machine learning-based methods, including logistic
regression, decision trees, random the forests, support to the
vector of the +machines, and XG Boost, with the purpose of
identifying credit card fraud. However, these methods often
exhibit lower accuracy rates, highlighting the need for more
advanced deep learning algorithms in order to effectively
lower fraud losses. Therefore, the of way they primary
thinks objective of this is to improve fraud detection
abilities through which the way this application most be of
state- of-the-art deep learning algorithms.
An assessment way it should be of the research project
reveals better outcomes, including optimized AUC curves,
precision, f1-score, and accuracy.
The ultimate objective there are is to create models
that will greatly improve credit card fraud detection and
prevention. This research focuses on advanced deep
learning techniques in to the way it is order they provide the
to more reliable and accurate fraud detection mechanisms,
enhance security for credit card users, and lower financial
risks for financial institutions when conducting online
transactions.
Keywords :
Transaction Data Analytics, Online Fraud, Credit Card Fraud, Deep Learning, Machine Learning, Fraud Detection.
