Credit Card Fraud Detection; A Machine Learning Approach
| dc.contributor.author | Glah, J. | |
| dc.date.accessioned | 2024-02-14T13:22:57Z | |
| dc.date.available | 2024-02-14T13:22:57Z | |
| dc.date.issued | 2020-11 | |
| dc.description | MPhil. Statistics | en_US |
| dc.description.abstract | In recent times, credit card usage has increased tremendously because it is convenient to use and also saves a lot of time. Credit cards are rectangular plastic cards issued by banks which allow a person to borrow funds from a pre - approved limit to pay for one’s purchases now and pay later. In the same manner, credit card frauds have also been on the increase causing huge sums of financial loss to credit card issuers. Credit card fraud is the use of a credit card by someone who is not the owner of the card and is not allowed to use it. In this study, three classification methods were used to do a deep analysis of credit card transactions history and the fraud detection models built. This study presents and demonstrates the advantages of support vector machine, artificial neural network and the k - nearest neighbor algorithms to the credit cards data for the purpose of reducing the bank’s losses. The results show that the linear support vector machine and k - nearest neighbor approaches outperform artificial neural network in solving the problem under investigation. This study allows for multiple algorithms to be integrated together as modules and their results combined to increase the accuracy of the final results. | en_US |
| dc.identifier.uri | http://ugspace.ug.edu.gh:8080/handle/123456789/41258 | |
| dc.language.iso | en | en_US |
| dc.publisher | University Of Ghana | en_US |
| dc.subject | Credit Card | en_US |
| dc.subject | Fraud | en_US |
| dc.subject | Learning | en_US |
| dc.title | Credit Card Fraud Detection; A Machine Learning Approach | en_US |
| dc.type | Thesis | en_US |
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