Credit Card Fraud Detection Using AdaBoost and Majority Voting
Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers
2018
|
| Subjects: |
| _version_ | 1796961333438054400 |
|---|---|
| author | Randhawa, Kuldeep Loo, Chu Kiong Seera, Manjeevan Lim, Chee Peng Nandi, Asoke K. |
| author_facet | Randhawa, Kuldeep Loo, Chu Kiong Seera, Manjeevan Lim, Chee Peng Nandi, Asoke K. |
| author_sort | Randhawa, Kuldeep |
| collection | UM |
| description | Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards. |
| first_indexed | 2024-03-06T05:52:40Z |
| format | Article |
| id | um.eprints-20919 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:52:40Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | um.eprints-209192019-04-16T02:19:47Z http://eprints.um.edu.my/20919/ Credit Card Fraud Detection Using AdaBoost and Majority Voting Randhawa, Kuldeep Loo, Chu Kiong Seera, Manjeevan Lim, Chee Peng Nandi, Asoke K. QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards. Institute of Electrical and Electronics Engineers 2018 Article PeerReviewed Randhawa, Kuldeep and Loo, Chu Kiong and Seera, Manjeevan and Lim, Chee Peng and Nandi, Asoke K. (2018) Credit Card Fraud Detection Using AdaBoost and Majority Voting. IEEE Access, 6. pp. 14277-14284. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2018.2806420 <https://doi.org/10.1109/ACCESS.2018.2806420>. https://doi.org/10.1109/ACCESS.2018.2806420 doi:10.1109/ACCESS.2018.2806420 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Randhawa, Kuldeep Loo, Chu Kiong Seera, Manjeevan Lim, Chee Peng Nandi, Asoke K. Credit Card Fraud Detection Using AdaBoost and Majority Voting |
| title | Credit Card Fraud Detection Using AdaBoost and Majority Voting |
| title_full | Credit Card Fraud Detection Using AdaBoost and Majority Voting |
| title_fullStr | Credit Card Fraud Detection Using AdaBoost and Majority Voting |
| title_full_unstemmed | Credit Card Fraud Detection Using AdaBoost and Majority Voting |
| title_short | Credit Card Fraud Detection Using AdaBoost and Majority Voting |
| title_sort | credit card fraud detection using adaboost and majority voting |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT randhawakuldeep creditcardfrauddetectionusingadaboostandmajorityvoting AT loochukiong creditcardfrauddetectionusingadaboostandmajorityvoting AT seeramanjeevan creditcardfrauddetectionusingadaboostandmajorityvoting AT limcheepeng creditcardfrauddetectionusingadaboostandmajorityvoting AT nandiasokek creditcardfrauddetectionusingadaboostandmajorityvoting |