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dc.contributor.authorNimashini, RMSM
dc.contributor.authorRathnayake, RMKT
dc.contributor.authorWickramaarachchi, WU
dc.date.accessioned2021-12-24T06:43:34Z
dc.date.available2021-12-24T06:43:34Z
dc.date.issued2021
dc.identifier.urihttp://ir.kdu.ac.lk/handle/345/5213
dc.description.abstractThe world is reaching a cashless society with the increment of non-cash transactions. E-commerce has become an essential factor in every organization in global trade. Since financial institutions co-operate with billions of online transactions per day, identifying fraudulent transactions has become a challenge. This research was mainly focused on identifying the best intelligent adaptive authentication technique for credit card fraud detection. Areal-world transaction dataset of European credit cardholders and a synthetic dataset were used to extract the historical transactional patterns using Artificial Neural Network (ANN). Different classification algorithms, Logistic Regression, Decision Tree, Random Forest and XGBoost were also used for a comparative analysis to classify a real-world dataset. Among all, ANN and XGBoost have shown the highest performance in the binary classification of fraud and legitimate transactions. ANN has shown an accuracy of 99.94% and high adaptability in handling large datasets, by giving zero misclassification of fraud as a legitimate transaction by reducing the risk to its minimum.en_US
dc.language.isoenen_US
dc.subjectfraud detectionen_US
dc.subjectANNen_US
dc.subjectadaptive authenticationen_US
dc.subjectrandom foresten_US
dc.subjectdecision treeen_US
dc.subjectXGBoosten_US
dc.subjectlogistic regressionen_US
dc.titleA Machine Learning Approach for Detecting Credit Card Fraudulent Transactionen_US
dc.typeArticle Full Texten_US
dc.identifier.journalKDU IRC, 2021en_US
dc.identifier.issueFaculty of Computingen_US
dc.identifier.pgnos158-165en_US


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