Chagwiza, W.Garira,W.Mashamba, Thanganedzo Beverly2023-11-162023-11-162023-10-05Mashamba, T. B. (2023).Credit Card Fraud Detection using Boosted Random Forest Algorithm. University of Venda, Thohoyandou, South Africa.<http://hdl.handle.net/11602/2651>.http://hdl.handle.net/11602/2651MSc (e-Science)Department of Mathematical and Computational SciencesFinancial fraud is a growing concern with far-reaching concerns in financial institutions, government, and corporate organizations, leading to substantial monetary losses. The primary cause of financial loss is credit card fraud; it affects issuers and clients, which is a significant threat to the business as clients will run to their competitors, wherein they will feel secure. Solving fraud problems is beyond human capability, so financial institutions can utilize machine learning algorithms to detect fraudulent behaviour by learning through credit card transactions. This thesis develops the boosted random forest, integrating an adaptive boosting algorithm into a random forest algorithm, such that the performance of a model is improved in predicting credit card fraudulent transactions. The confusion matrix is used to evaluate the performance of the models, wherein random forest, adaptive boosting and boosted random forest were compared. The results indicated that the boosted random forest outperformed the individual models with an accuracy of 99.9%, which corresponded with the results from confusion matrix. However random forest and adaptive boosting had 100% and 99% respectively, which did not correspond to the results on confusion matrix, meaning the individual models need to be more accurate. Thus, by implementing the proposed approach to a credit card management system, financial loss will be reduced to a greater extent.1 online resource (viii, 60 leaves)enUniversity of VendaAdaptive boostingUCTDCredit cardEvaluation matricesFraudRandom forest332.10684Credit card fraudCredit cards -- ForgeriesCommercial crimesFraudBank fraudDissertationMashamba TB. Credit Card Fraud Detection using Boosted Random Forest Algorithm. []. , 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2651Mashamba, T. B. (2023). <i>Credit Card Fraud Detection using Boosted Random Forest Algorithm</i>. (). . Retrieved from http://hdl.handle.net/11602/2651Mashamba, Thanganedzo Beverly. <i>"Credit Card Fraud Detection using Boosted Random Forest Algorithm."</i> ., , 2023. http://hdl.handle.net/11602/2651TY - Dissertation AU - Mashamba, Thanganedzo Beverly AB - Financial fraud is a growing concern with far-reaching concerns in financial institutions, government, and corporate organizations, leading to substantial monetary losses. The primary cause of financial loss is credit card fraud; it affects issuers and clients, which is a significant threat to the business as clients will run to their competitors, wherein they will feel secure. Solving fraud problems is beyond human capability, so financial institutions can utilize machine learning algorithms to detect fraudulent behaviour by learning through credit card transactions. This thesis develops the boosted random forest, integrating an adaptive boosting algorithm into a random forest algorithm, such that the performance of a model is improved in predicting credit card fraudulent transactions. The confusion matrix is used to evaluate the performance of the models, wherein random forest, adaptive boosting and boosted random forest were compared. The results indicated that the boosted random forest outperformed the individual models with an accuracy of 99.9%, which corresponded with the results from confusion matrix. However random forest and adaptive boosting had 100% and 99% respectively, which did not correspond to the results on confusion matrix, meaning the individual models need to be more accurate. Thus, by implementing the proposed approach to a credit card management system, financial loss will be reduced to a greater extent. DA - 2023-10-05 DB - ResearchSpace DP - Univen KW - Adaptive boosting KW - Credit card KW - Evaluation matrices KW - Fraud KW - Random forest LK - https://univendspace.univen.ac.za PY - 2023 T1 - Credit Card Fraud Detection using Boosted Random Forest Algorithm TI - Credit Card Fraud Detection using Boosted Random Forest Algorithm UR - http://hdl.handle.net/11602/2651 ER -