{"title":"Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation","authors":"Syeda Farjana Farabi, ✉. M. Prabha, Mahfuz Alam, Md Zikar Hossan, Md Rafiqul Islam, Aftab Uddin, Maniruzzaman Bhuiyan, Md Zinnat, Ali Biswas","doi":"10.32996/jbms.2024.6.13.21","DOIUrl":null,"url":null,"abstract":"Credit card fraud detection remains a significant challenge for financial institutions and consumers globally, prompting the adoption of advanced data analytics and machine learning techniques. In this study, we investigate the methodology and performance evaluation of various machine learning algorithms for credit card fraud detection, emphasizing data preprocessing techniques and model effectiveness. Through thorough dataset analysis and experimentation using cross-validation approaches, we assess the performance of logistic regression, decision trees, random forest classifiers, Naïve Bayes classifiers, K-nearest neighbors (KNN), and artificial neural networks (ANN-DL). Key performance metrics such as accuracy, sensitivity, specificity, and F1-score are compared to identify the most effective models for detecting fraudulent transactions. Additionally, we explore the impact of different folds in cross-validation on model performance, providing insights into the classifiers' robustness and stability. Our findings contribute to the ongoing efforts to develop efficient fraud detection systems, offering valuable insights for financial institutions and researchers striving to combat credit card fraud effectively.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":"39 7","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business and Management Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32996/jbms.2024.6.13.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Credit card fraud detection remains a significant challenge for financial institutions and consumers globally, prompting the adoption of advanced data analytics and machine learning techniques. In this study, we investigate the methodology and performance evaluation of various machine learning algorithms for credit card fraud detection, emphasizing data preprocessing techniques and model effectiveness. Through thorough dataset analysis and experimentation using cross-validation approaches, we assess the performance of logistic regression, decision trees, random forest classifiers, Naïve Bayes classifiers, K-nearest neighbors (KNN), and artificial neural networks (ANN-DL). Key performance metrics such as accuracy, sensitivity, specificity, and F1-score are compared to identify the most effective models for detecting fraudulent transactions. Additionally, we explore the impact of different folds in cross-validation on model performance, providing insights into the classifiers' robustness and stability. Our findings contribute to the ongoing efforts to develop efficient fraud detection systems, offering valuable insights for financial institutions and researchers striving to combat credit card fraud effectively.
信用卡欺诈检测仍然是全球金融机构和消费者面临的一项重大挑战,促使人们采用先进的数据分析和机器学习技术。在本研究中,我们研究了用于信用卡欺诈检测的各种机器学习算法的方法和性能评估,强调了数据预处理技术和模型的有效性。通过全面的数据集分析和使用交叉验证方法的实验,我们评估了逻辑回归、决策树、随机森林分类器、奈夫贝叶斯分类器、K-近邻(KNN)和人工神经网络(ANN-DL)的性能。通过比较准确率、灵敏度、特异性和 F1 分数等关键性能指标,我们找出了检测欺诈交易的最有效模型。此外,我们还探讨了交叉验证中不同折叠对模型性能的影响,从而深入了解分类器的鲁棒性和稳定性。我们的研究结果为开发高效欺诈检测系统的持续努力做出了贡献,为金融机构和研究人员有效打击信用卡欺诈提供了宝贵的见解。