Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative survey
Date
2021-06-30
Authors
Rahman, Rizwana
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Abstract
Credit cards play a vital role in our day to day lives all over the world. Credit
cards contain sensitive data and credit card frauds can affect not only the owner of
the card but banks, government, and all type of financial organizations that causes in
high financial losses. The numbers are expected to increase in the future, and for that
reason, many researchers in this field have focused on detecting fraudulent behaviors
early using various machine learning techniques. Basically, credit card fraudulent
transaction detection becomes troublesome because of the two main reasons: one
of them is highly imbalanced data set and the other one is the fraudulent behav-
iors usually differ for each attempt, so it is hard to find specific pattern/ features
for each fraudulent transaction. To get rid of these issues the algorithms like Lo-
gistic Regression, Na ̈ıve Bayes, Random Forest, K- Nearest Neighbor, and Decision
tree (classification) algorithms can be used for detecting fraudulent transactions. A
Comparative analysis is performed to find out which algorithm model performs best
among them using different performance matrices like precision, recall, f1 score and
provide an optimal solution. We have used data set from Kaggle, which is the highly
imbalanced data set of credit card transactions of European credit cardholders of
2013. To balance the data set, Over Sampling, Under Sampling SMOTE (Synthetic
Minority Oversampling Technique) techniques are used. Three re sampling techniques
e.g. Over sampling, Under sampling and Smote are used to compare the results to
know which one performs better. We have used Python programming language and
JUPYTER NOTEBOOK IDE to perform comparison and view results.
Description
Keywords
Credit Card, fraud