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Online Payment Fraud Detection using Machine Learning Techniques

Chawla, Taranjyot Singh (2023) Online Payment Fraud Detection using Machine Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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When it comes to the simplicity of making a payment while sitting anywhere in the world, online payments have been a source of attractiveness. Over the past few decades, there has been an increase in online payments. E-payments enable businesses earn a lot of money in addition to consumers. However, because electronic payments are so simple, there is also a risk of fraud associated with them. A consumer must ensure that the payment he is paying is going exclusively to the appropriate service provider. Online fraud exposes users to the possibility of their data being compromised, as well as the inconvenience of having to report the fraud, block their payment method, and other things. When businesses are involved, it causes some issues; occasionally, they must issue refunds in order to keep customers. Therefore, it is crucial that both consumers and businesses are aware of these internet scams. A model to determine if an online payment is fraudulent or not is put forth in this study. To determine if a certain Online payment is fraudulent or not, some features like the type of payment, the recipient’s identity, etc. would be taken into account.

Item Type: Thesis (Masters)
Basilio, Jorge
Subjects: H Social Sciences > HF Commerce
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 17 May 2023 12:52
Last Modified: 17 May 2023 12:52

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