Authors :
Rajeev Kumar, Rajesh Budihul
Volume/Issue :
Volume 5 - 2020, Issue 4 - April
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2Wk8EOn
Abstract :
The objectives of this research paper, the
topic of credit card fraud detection has gained and
developed fraudsters are increasing day by day among
researches because of their frequent look in varied and
widespread application within the field of various
branches of information technology and engineering.
For example, genetic algorithms, Behavior-based
techniques, and Hidden Marks models are also used to
address these problems of technology. Credit card fraud
detection models for transactions are tested individually
and proceed to whatever is most effective. The aim of
this thesis is to develop some method of detecting
fraudulent transactions and producing test dataset.
These algorithms are the predictive method in solving
high complication computational problems.
Keywords :
Fraud detection of credit card; Naive Bayes, KNearest Neighbors and Logistic Regression Classifier; Hidden Markov Model; K-means Clustering; GMDH; DST; Bayesian learning and Neural Network.
The objectives of this research paper, the
topic of credit card fraud detection has gained and
developed fraudsters are increasing day by day among
researches because of their frequent look in varied and
widespread application within the field of various
branches of information technology and engineering.
For example, genetic algorithms, Behavior-based
techniques, and Hidden Marks models are also used to
address these problems of technology. Credit card fraud
detection models for transactions are tested individually
and proceed to whatever is most effective. The aim of
this thesis is to develop some method of detecting
fraudulent transactions and producing test dataset.
These algorithms are the predictive method in solving
high complication computational problems.
Keywords :
Fraud detection of credit card; Naive Bayes, KNearest Neighbors and Logistic Regression Classifier; Hidden Markov Model; K-means Clustering; GMDH; DST; Bayesian learning and Neural Network.
