Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods - Sunil Kumar Patel; Devina Panday (Advances in Distributed Computing and Artificial Intelligence Journal)
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.