TY  - JOUR
T1  - Machine Learning for Credit Card Fraud Detection
AU - Ghalwash, Atef Zaki AU - Khalil Taktak, Sameh Gamal AU - Galal, Amr AU - M. Abbaassy, Mohamed 
JO  - Asian Journal of Information Technology
VL  - 21
IS  - 2
SP  - 6
EP  - 10
PY  - 2022
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2022.6.10
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2022.6.10
KW  - decision tree
KW  -SVM
KW  -machine learning
KW  -Credit fraud detection
KW  -logistics regression
AB  - Due to the increased use of credit cards for
online purchases, fraud has increased exponentially
because of the rapid rise in the e-commerce business. In
recent years, banks have found it increasingly difficult to
detect credit card fraud. The power of machine
intelligence can detect credit card fraud. Banks have used
a variety of machine learning approaches, prior data and
novel attributes to better forecast these transactions. For
credit card transactions, the sampling method used, as
well as the selection of data points and the detection
techniques used, can all have a significant impact on fraud
detection. Credit card fraud is investigated using logistic
regression, decision trees, random forests and Support
Vector Machines (SVM). In September 2013, the data
included all transactions made by European cardholders.
There were 492 instances of fraud out of a total of
284, 807 transactions. It classifies fraudulent transactions
as "positive" and legitimate transactions as "negative”.
Fraud accounts for 0.173% of the total transactions in the
data set, making it highly imbalanced. To balance the data
set, over sampling was used, resulting in 60% of
fraudulent transactions and 40% of genuine transactions.
Among the four models, Logistic Regression produced
the best results, the Logistic Regression model has a 96%
accuracy.
ER  -