Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods
In today’s cashless society, the increasing threat of credit card fraud demands our attention. To protect our financial security, it is crucial to develop robust and accurate fraud detection systems that stay one step ahead of the fraudsters. This study dives into the realm of machine learning, eval...
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Ediciones Universidad de Salamanca
2024-12-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
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| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31533 |
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| author | Sunil Kumar Patel Devina Panday |
| author_facet | Sunil Kumar Patel Devina Panday |
| author_sort | Sunil Kumar Patel |
| collection | DOAJ |
| description | In today’s cashless society, the increasing threat of credit card fraud demands our attention. To protect our financial security, it is crucial to develop robust and accurate fraud detection systems that stay one step ahead of the fraudsters. This study dives into the realm of machine learning, evaluating the performance of various algorithms - logistic regression (LR), decision tree (DT), and random forest (RF) - in detecting credit card fraud. Taking innovation, a step further, the study introduces the integration of a genetic algorithm (GA) for feature selection and optimization alongside LR, DT, and RF models. LR achieved an accuracy of 99.89 %, DT outperformed with an accuracy of 99.936 %, and RF yielded a high accuracy of 99.932 %, whereas GA-RF (a5) achieved an accuracy of 99.98 %. Ultimately, the findings of this study fuel the development of more potent fraud detection systems within the realm of financial institutions, safeguarding the integrity of transactions and ensuring peace of mind for cardholders. |
| format | Article |
| id | doaj-art-8a9570482c4248e9a044686ed6f8b1a2 |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-8a9570482c4248e9a044686ed6f8b1a22025-01-23T11:25:18ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-12-0113e31533e3153310.14201/adcaij.3153337014Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection MethodsSunil Kumar Patel0Devina Panday1Department of Computer Science and Engineering, School of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India, 303007Department of Computer Science and Engineering, School of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India- 303007In today’s cashless society, the increasing threat of credit card fraud demands our attention. To protect our financial security, it is crucial to develop robust and accurate fraud detection systems that stay one step ahead of the fraudsters. This study dives into the realm of machine learning, evaluating the performance of various algorithms - logistic regression (LR), decision tree (DT), and random forest (RF) - in detecting credit card fraud. Taking innovation, a step further, the study introduces the integration of a genetic algorithm (GA) for feature selection and optimization alongside LR, DT, and RF models. LR achieved an accuracy of 99.89 %, DT outperformed with an accuracy of 99.936 %, and RF yielded a high accuracy of 99.932 %, whereas GA-RF (a5) achieved an accuracy of 99.98 %. Ultimately, the findings of this study fuel the development of more potent fraud detection systems within the realm of financial institutions, safeguarding the integrity of transactions and ensuring peace of mind for cardholders.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31533credit card fraudlogistic regressiondecision treerandom forestgenetic algorithmoptimizationaccuracyprecision |
| spellingShingle | Sunil Kumar Patel Devina Panday Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods Advances in Distributed Computing and Artificial Intelligence Journal credit card fraud logistic regression decision tree random forest genetic algorithm optimization accuracy precision |
| title | Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods |
| title_full | Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods |
| title_fullStr | Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods |
| title_full_unstemmed | Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods |
| title_short | Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods |
| title_sort | optimizing credit card fraud detection a genetic algorithm approach with multiple feature selection methods |
| topic | credit card fraud logistic regression decision tree random forest genetic algorithm optimization accuracy precision |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31533 |
| work_keys_str_mv | AT sunilkumarpatel optimizingcreditcardfrauddetectionageneticalgorithmapproachwithmultiplefeatureselectionmethods AT devinapanday optimizingcreditcardfrauddetectionageneticalgorithmapproachwithmultiplefeatureselectionmethods |