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Novel Machine Learning Approach for Analyzing Anonymous Credit Card Fraud Patterns

International Journal of Electronic Commerce Studies

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Title Novel Machine Learning Approach for Analyzing Anonymous Credit Card Fraud Patterns
 
Creator Manlangit, Sylvester; Charles Darwin University
Azam, Sami; Charles Darwin University
Shanmugam, Bharanidharan; Charles Darwin University
karim, Asif; Charles Darwin University
 
Subject Fraudulent Credit Card Transactions; k-NN; SMOTE; PCA; Machine Learning
 
Description Fraudulent credit card transactions are on the rise and have become a significantly problematic issue for financial intuitions and individuals. Various methods have already been implemented to handle the issue, but the embezzlers have always managed to employ innovative tactics to circumvent a number of security measures and execute the fraudulent transactions. Thus, instead of a rule-based system, an intelligent and adaptable machine learning based algorithm should be an answer to tackle such sophisticated digital theft. The presented framework uses k-NN for classification and utilises Principal Component Analysis (PCA) for raw data transformation. Neighbours (anomalies in data) were created using Synthetic Minority Oversampling Technique (SMOTE) and a distance-based feature selection method was employed. The proposed process performed well by having a precision and F-Score of 98.32% and 97.44% respectively for k-NN and 100% and 98.24% respectively for Time subset when using the misclassified instances. This work also demonstrates a larger and clearer classification breakdown, which aids in achieving higher precision rate and improved recall rate. In a view to accomplish such high accuracy, the original datum was transformed using Principal Component Analysis (PCA), neighbours (anomalies in data) were created using Synthetic Minority Oversampling Technique (SMOTE) and a distance based feature selection method was employed. The proposed process performed well when using the misclassified instances in the test dataset used in the previous work, while demonstrating a larger and clearer classification breakdown.To cite this document: Sylvester Manlangit, Sami Azam, Bharanidharan Shanmugam, and Asif karim, "Novel Machine Learning Approach for Analyzing Anonymous Credit Card Fraud Patterns", International Journal of Electronic Commerce Studies, Vol.10, No.2, pp.175-202, 2019.Permanent link to this document:http://dx.doi.org/10.7903/ijecs.1732
 
Publisher Academy of Taiwan Information Systems Research
 
Contributor —
 
Date 2019-12-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
—
Technology Development
 
Format application/pdf
 
Identifier http://academic-pub.org/ojs/index.php/ijecs/article/view/1732
10.7903/ijecs.1732
 
Source "International Journal of Electronic Commerce Studies"; Vol 10, No 2 (2019); 175-202
2073-9729
 
Language eng
 
Relation http://academic-pub.org/ojs/index.php/ijecs/article/view/1732/366
 
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