Authors :
Sarthak Aggarwal; Vibhuti Nautiyal; Garima Joshi; Nishit Galhotra
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/mr3uwwn5
DOI :
https://doi.org/10.5281/zenodo.8058763
Abstract :
It is difficult for credit card firms to detect
malicious activities like fraudulent transactions which
cause its users to make payments from their accounts
without their knowledge for the items that they did not
purchase leading them to financial loss. As the world is
moving towards digitalization the use of digital money
has also increased which has also led to a rise in fraud
associated with them parallelly. There are several
methods applied to stop fraudulent activities but
fraudsters keep on trying to find new ways and methods
and always come up with unique ideas to break the
security mechanism to commit fraudulent transactions
making billions of losses to banks and credit card users
globally. Therefore, there is a great demand for a
technique for detecting credit card made fraudulent
transaction that not only prevents it but also accurately
and efficiently anticipates before it happens. This paper
uses and explains various techniques for detecting credit
card fraud, conducts a thorough analysis of both the
existing models and the proposed model, and then
conducts a comparison of these techniques based on
achieved accuracy, false alarm rate, and detection rate.
Keywords :
Random Forest, Logistic Regression, Decision Tree, SVM (Support Vector Mechanism), False Alarm Rate (FAR), Decision Rate.
It is difficult for credit card firms to detect
malicious activities like fraudulent transactions which
cause its users to make payments from their accounts
without their knowledge for the items that they did not
purchase leading them to financial loss. As the world is
moving towards digitalization the use of digital money
has also increased which has also led to a rise in fraud
associated with them parallelly. There are several
methods applied to stop fraudulent activities but
fraudsters keep on trying to find new ways and methods
and always come up with unique ideas to break the
security mechanism to commit fraudulent transactions
making billions of losses to banks and credit card users
globally. Therefore, there is a great demand for a
technique for detecting credit card made fraudulent
transaction that not only prevents it but also accurately
and efficiently anticipates before it happens. This paper
uses and explains various techniques for detecting credit
card fraud, conducts a thorough analysis of both the
existing models and the proposed model, and then
conducts a comparison of these techniques based on
achieved accuracy, false alarm rate, and detection rate.
Keywords :
Random Forest, Logistic Regression, Decision Tree, SVM (Support Vector Mechanism), False Alarm Rate (FAR), Decision Rate.
