What is it about?
This study investigates methodologies for detecting fraudulent activities in online credit card transactions. The researchers employed a decision tree algorithm to analyze historical transaction data. The algorithm demonstrated proficiency in identifying suspicious patterns with minimal errors during testing. The findings suggest that the integration of such technology holds the potential to enhance the security of online transactions by reducing the occurrences of both false positives, erroneously identifying legitimate transactions as fraudulent, and false negatives, failing to detect actual fraudulent activities.
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Why is it important?
The findings of the paper have practical implications for businesses and individuals, emphasizing the potential of machine learning in enhancing fraud detection systems. The study contributes to the growing body of knowledge on leveraging supervised learning techniques for fraud prevention.
Perspectives
The paper presents a compelling case for the application of decision trees in credit card fraud detection, showcasing its effectiveness in reducing false positives and false negatives. This research can be valuable for financial institutions and businesses seeking robust solutions to enhance the security of online transactions.
Mahwish Ashraf
University of Fujairah
Read the Original
This page is a summary of: A Fraud Detection System Using Decision Trees Classification in An Online Transactions, February 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3587828.3587860.
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