ISSN 2394-5125
 

Research Article 


A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari.

Abstract
For both online and offline purchases, Credit cards are one of the most used and popular modes of payment which is also leading to increasing daily fraud transactions.It results in huge financial losses as these events take place frequently. Number of online transactions has grown in large quantities where online transactions hold a huge share of these transactions. Thus, credit card fraud detection applications are offered by banks and financial institutions. In order to maintain the reliability of the payment system, an efficient fraud detection methodology is essential. Various Machine learning algorithms have increased the capacity of handling a huge dataset which helps in detecting many fraudulent transactions very efficiently along with its high power of processing and computing data. For real time problems which occur in our daily life Machine Learning provides fast and efficient solutions. In this comparative study, credit card fraud detection is performed on the huge datasets which involves lakhs of rows of transaction data by using various Machine Learning Algorithms as it consumes lot of time to manually check and find the patterns in the data for fraudulent transactions. Due to an imbalanced dataset, SMOTE technique is used for oversampling. Further, feature selection is performed and the dataset is split into two parts, training data and test data. Machine Learning models used are: Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbors (KNN), Random Forest (RF), Artificial Neural Networks (ANN).

Key words: Credit Card, Fraud Detection, Train, Test, Split, Classifier.


 
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How to Cite this Article
Pubmed Style

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari. A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING. JCR. 2020; 7(19): 998-1011. doi:10.31838/jcr.07.19.124


Web Style

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari. A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING. http://www.jcreview.com/?mno=104019 [Access: July 17, 2020]. doi:10.31838/jcr.07.19.124


AMA (American Medical Association) Style

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari. A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING. JCR. 2020; 7(19): 998-1011. doi:10.31838/jcr.07.19.124



Vancouver/ICMJE Style

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari. A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING. JCR. (2020), [cited July 17, 2020]; 7(19): 998-1011. doi:10.31838/jcr.07.19.124



Harvard Style

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari (2020) A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING. JCR, 7 (19), 998-1011. doi:10.31838/jcr.07.19.124



Turabian Style

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari. 2020. A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING. Journal of Critical Reviews, 7 (19), 998-1011. doi:10.31838/jcr.07.19.124



Chicago Style

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari. "A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING." Journal of Critical Reviews 7 (2020), 998-1011. doi:10.31838/jcr.07.19.124



MLA (The Modern Language Association) Style

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari. "A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING." Journal of Critical Reviews 7.19 (2020), 998-1011. Print. doi:10.31838/jcr.07.19.124



APA (American Psychological Association) Style

Prerana Berad, Shivani Parihar, Zuber Lakhani, Anjali Kshirsagar, Anagha Chaudhari (2020) A COMPARATIVE STUDY: CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING. Journal of Critical Reviews, 7 (19), 998-1011. doi:10.31838/jcr.07.19.124





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