Online payment fraud prediction with machine learning approach using naive bayes algorithm - Rahman, Raihan Muhammad Rizki; Muslim, Much Aziz (Journal of Student Research Exploration, 2024)
The increase in e-commerce has provided easy access for the public, but it also opens up opportunities for fraud in online transactions. Payment fraud is also a problem that often arises in transactions through electronic media. This research aims to analyze payment fraud in e-commerce transactions. This research uses a machine learning approach using the Naive Bayes algorithm. This research uses online transaction datasets involving various attributes such as payment and shipping methods. The developed Naive Bayes model achieved an accuracy of 61.03% with K = 7. The evaluation shows a balance between precision (59.46%) and recall (62.05%), although this study is limited by data quality and basic assumptions of Naive Bayes. In future research, it is worth considering the use of additional features or more complex data processing to improve the performance of fraud detection in online transactions. This research provides important insights in the fight against financial crime in the context of electronic commerce.